WO2021000587A1

WO2021000587A1 - Vehicle door unlocking method and device, system, vehicle, electronic equipment and storage medium

Info

Publication number: WO2021000587A1
Application number: PCT/CN2020/076713
Authority: WO
Inventors: 李轲; 林琪钧
Original assignee: 上海商汤智能科技有限公司
Priority date: 2019-07-01
Filing date: 2020-02-26
Publication date: 2021-01-07
Also published as: CN110335389A; JP2022537923A; KR20220070581A; JP2022118730A; KR20220016184A; CN110335389B

Abstract

Provided are a vehicle door unlocking method and device, a system, a vehicle, an electronic equipment and a storage medium. The method comprises: searching a bluetooth device with a preset identifier via a bluetooth module disposed in a vehicle (S11); in response to finding the bluetooth device with the preset identifier, establishing a bluetooth pairing connection between the bluetooth module and the bluetooth device with the preset identifier (S12); in response to successful bluetooth pairing connection, waking up and controlling an image acquisition moduledisposed in the vehicle to acquirea first image of a target object (S13); performing face recognition on the basis of the first image (S14); and in response to successful face recognition, sending a vehicle door unlocking instruction and/or a vehicle door opening instruction to at least one vehicle door (S15) of the vehicle. The technical solution can satisfythe requirements of low power consumption, quickopening of the vehicle door and improvement of user experience.

Description

Vehicle door unlocking method and device, system, vehicle, electronic equipment and storage medium

This application requires the priority of a Chinese patent application filed with the Chinese Patent Office on July 1, 2019, the application number is 201910586845.6, and the application name is "car door unlocking method and device, system, vehicle, electronic equipment and storage medium", all of which The content is incorporated in this application by reference.

Technical field

The present disclosure relates to the field of vehicle technology, and in particular to a method and device for unlocking a vehicle door, a system, a vehicle, an electronic device, and a storage medium.

Background technique

Brushing the face to open the door is a new technology for smart vehicles. At present, in order to be able to detect the face in time, the camera needs to be kept open; in order to be able to determine whether a person approaching the vehicle is the owner of the car in time, the image collected by the camera needs to be processed in real time to quickly identify the owner to quickly open the door. However, this method has high operating power consumption, and long-term high-power operation may cause the vehicle to fail to start due to insufficient power, which will affect the normal use of the vehicle and the user experience.

Summary of the invention

The present disclosure proposes a technical solution for unlocking a vehicle door.

According to a first aspect of the present disclosure, there is provided a method for unlocking a vehicle door, including:

Search for Bluetooth devices with preset identification via the Bluetooth module installed in the car;

In response to searching for the Bluetooth device with the preset identifier, establishing a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identifier;

In response to the successful Bluetooth pairing connection, waking up and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object;

Performing face recognition based on the first image;

In response to successful face recognition, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.

According to a second aspect of the present disclosure, there is provided a method for unlocking a vehicle door, including:

In response to searching for the Bluetooth device with the preset identifier, awakening and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object;

Performing face recognition based on the first image;

According to the disclosed third aspect, a vehicle door unlocking device is provided, including:

The search module is used to search for the Bluetooth device with the preset identification via the Bluetooth module installed in the car;

The wake-up module is used to establish a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification in response to searching for the Bluetooth device with the preset identification, and to wake up and control in response to the successful Bluetooth pairing connection The image acquisition module provided in the vehicle collects the first image of the target object, or, in response to searching for the Bluetooth device with the preset identification, wakes up and controls the image acquisition module provided in the vehicle to acquire the target object First image

A face recognition module, configured to perform face recognition based on the first image;

The unlocking module is used for sending a door unlocking instruction and/or opening a door instruction to at least one door of the vehicle in response to successful face recognition.

According to a fourth aspect of the present disclosure, a vehicle-mounted face unlocking system is provided, including: a memory, a face recognition module, an image acquisition module, and a Bluetooth module; the face recognition module is connected to the memory and the Bluetooth module, respectively. The image acquisition module is connected to the Bluetooth module; the Bluetooth module includes a device for waking up the face recognition module when the Bluetooth pairing connection with the Bluetooth device with the preset identification succeeds or the Bluetooth device with the preset identification is searched A microprocessor and a Bluetooth sensor connected to the microprocessor; the face recognition module is also provided with a communication interface for connecting with the door domain controller, and if the face recognition is successful, the The door domain controller sends control information for unlocking the door.

According to a fifth aspect of the present disclosure, there is provided a vehicle including the vehicle-mounted face unlocking system, and the vehicle-mounted face unlocking system is connected to a door domain controller of the vehicle.

According to a sixth aspect of the present disclosure, there is provided an electronic device, including:

processor;

A memory for storing processor executable instructions;

Wherein, the processor is configured to execute the method of the first aspect described above.

According to a seventh aspect of the present disclosure, there is provided an electronic device including:

processor;

A memory for storing processor executable instructions;

Wherein, the processor is configured to execute the method of the second aspect described above.

According to an eighth aspect of the present disclosure, there is provided a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method of the first aspect described above is implemented.

According to a ninth aspect of the present disclosure, there is provided a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method of the second aspect described above is implemented.

According to a tenth aspect of the present disclosure, there is provided a computer program including computer readable code, and when the computer readable code is executed in an electronic device, a processor in the electronic device executes to implement the above method.

In the embodiment of the present disclosure, the Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification is established in response to searching for the Bluetooth device with the preset identification, and in response to the successful Bluetooth pairing and connection, the face recognition module is awakened and controlled The image acquisition module collects the first image of the target object, and thus based on the successful Bluetooth pairing connection and then wakes up the face recognition module, it can effectively reduce the probability of falsely waking up the face recognition module, thereby improving user experience and effectively reducing The power consumption of the face recognition module. In addition, compared with short-range sensor technologies such as ultrasonic and infrared, the Bluetooth-based pairing connection method has the advantages of high security and support for larger distances. Practice has shown that the time when a user carrying a Bluetooth device with a preset logo reaches the car through this distance (the distance between the user and the car when the Bluetooth pairing connection is successful), and when the car wakes up, the face recognition module switches from a sleep state to a working state When the user arrives at the car door, the face recognition module can be used to recognize the car door immediately without having to wait for the face recognition module to be awakened after the user arrives at the car door. Improve the efficiency of face recognition and improve user experience. In addition, the user has no perception during the Bluetooth pairing and connection process, which can further improve the user experience. Therefore, the embodiments of the present disclosure provide a solution that can better weigh the face recognition module's power saving, user experience, and security by successfully waking up the face recognition module based on the Bluetooth pairing connection.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, rather than limiting the present disclosure.

According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear.

Description of the drawings

The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments that conform to the disclosure and are used together with the specification to explain the technical solutions of the disclosure.

Fig. 1 shows a flowchart of a method for unlocking a vehicle door according to an embodiment of the present disclosure.

Figure 2 shows a schematic diagram of the B-pillar of the car.

FIG. 3 shows a schematic diagram of the installation height and the recognizable height range of the vehicle door unlocking device in the vehicle door unlocking method according to an embodiment of the present disclosure.

Fig. 4a shows a schematic diagram of an image sensor and a depth sensor in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

Fig. 4b shows another schematic diagram of an image sensor and a depth sensor in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 5 shows a schematic diagram of an example of a living body detection method according to an embodiment of the present disclosure.

FIG. 6 shows a schematic diagram of an example of determining the result of the living body detection of the target object in the first image based on the first image and the second depth map in the living body detection method according to an embodiment of the present disclosure.

Fig. 7 shows a schematic diagram of a depth prediction neural network in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 8 shows a schematic diagram of a correlation detection neural network in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

Fig. 9 shows an exemplary schematic diagram of updating the depth map in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 10 shows a schematic diagram of surrounding pixels in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 11 shows another schematic diagram of surrounding pixels in a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 12 shows another flowchart of a method for unlocking a vehicle door according to an embodiment of the present disclosure.

FIG. 13 shows a block diagram of a vehicle door unlocking device according to an embodiment of the present disclosure.

Fig. 14 shows a block diagram of a vehicle face unlocking system according to an embodiment of the present disclosure.

Fig. 15 shows a schematic diagram of a vehicle face unlocking system according to an embodiment of the present disclosure.

FIG. 16 shows a schematic diagram of a car according to an embodiment of the present disclosure.

Fig. 17 is a block diagram showing an electronic device 800 according to an exemplary embodiment.

Detailed ways

Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the drawings. The same reference numerals in the drawings indicate elements with the same or similar functions. Although various aspects of the embodiments are shown in the drawings, unless otherwise noted, the drawings are not necessarily drawn to scale.

The dedicated word "exemplary" here means "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" need not be construed as being superior or better than other embodiments.

The term "and/or" in this article is only an association relationship describing associated objects, which means that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, exist alone B these three situations. In addition, the term "at least one" in this document means any one or any combination of at least two of the multiple, for example, including at least one of A, B, and C, may mean including A, Any one or more elements selected in the set formed by B and C.

In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some instances, the methods, means, elements, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the gist of the present disclosure.

Fig. 1 shows a flowchart of a method for unlocking a vehicle door according to an embodiment of the present disclosure. The vehicle door unlocking method may be executed by a vehicle door unlocking device. For example, the method for unlocking the vehicle door may be executed by an in-vehicle device or other processing device. For example, the vehicle door unlocking device may be installed in at least one of the following positions: a B-pillar of a vehicle, at least one vehicle door, and at least one rearview mirror. Figure 2 shows a schematic diagram of the B-pillar of the car. For example, the door unlocking device can be installed on the B-pillar from 130 cm to 160 cm above the ground, and the horizontal recognition distance of the door unlocking device can be 30 cm to 100 cm, which is not limited here. FIG. 3 shows a schematic diagram of the installation height and the recognizable height range of the vehicle door unlocking device in the vehicle door unlocking method according to an embodiment of the present disclosure. In the example shown in FIG. 3, the installation height of the door unlocking device is 160 cm, and the recognizable height range is 140 cm to 190 cm.

In a possible implementation manner, the method for unlocking the vehicle door may be implemented by a processor calling a computer readable instruction stored in the memory.

As shown in Fig. 1, the method for unlocking the vehicle door includes steps S11 to S15.

In step S11, a Bluetooth device with a preset identification is searched through the Bluetooth module installed in the car.

In a possible implementation manner, searching for a Bluetooth device with a preset identifier via a Bluetooth module installed in the car includes: searching for a preset identifier via a Bluetooth module installed in the car when the car is turned off or in a state where the door is locked. Set the identified Bluetooth device. In this implementation, there is no need to search for a Bluetooth device with a preset identification through the Bluetooth module before the car is turned off, or there is no need to search for a preset identification through the Bluetooth module before the car is turned off and when the car is turned off but the door is not locked. Bluetooth devices, which can further reduce power consumption.

In a possible implementation manner, the Bluetooth module may be a Bluetooth Low Energy (BLE, Bluetooth Low Energy) module. In this implementation, when the car is in the off state or in the off state and the door is locked, the Bluetooth module can be in the broadcast mode and broadcast a broadcast data packet to the surroundings at regular intervals (for example, 100 milliseconds). When the surrounding Bluetooth devices are performing the scan action, if they receive the broadcast data packet broadcast by the Bluetooth module, they will send a scan request to the Bluetooth module. The Bluetooth module can respond to the scan request and return the scan to the Bluetooth device that sent the scan request. Response packet. In this implementation manner, if a scan request sent by a Bluetooth device with a preset identification is received, it is determined that the Bluetooth device with the preset identification is searched.

In another possible implementation, the Bluetooth module can be in the scanning state when the car is turned off or when the car is turned off and the door is locked. If a Bluetooth device with a preset logo is scanned, it is determined that the device with the preset logo is found. Bluetooth device.

In a possible implementation, the Bluetooth module and the face recognition module can be integrated in the face recognition system.

In another possible implementation, the Bluetooth module can be independent of the face recognition system. That is, the Bluetooth module can be installed outside the face recognition system.

The embodiment of the present disclosure does not limit the maximum search distance of the Bluetooth module. In an example, the maximum search distance may be about 30 m.

In the embodiments of the present disclosure, the identification of the Bluetooth device may refer to the unique identifier of the Bluetooth device. For example, the identification of the Bluetooth device may be the ID, name or address of the Bluetooth device.

In the embodiment of the present disclosure, the preset identification may be an identification of a device that is successfully paired with the Bluetooth module of the car based on the Bluetooth secure connection technology.

In the embodiment of the present disclosure, the number of Bluetooth devices with preset identification may be one or more. For example, if the identification of the Bluetooth device is the ID of the Bluetooth device, one or more Bluetooth IDs with permission to drive the door can be preset. For example, when the number of Bluetooth devices with preset identification is one, the Bluetooth device with preset identification may be the Bluetooth device of the vehicle owner; when the number of Bluetooth devices with preset identification is multiple, the multiple The Bluetooth devices with the preset identification may include the Bluetooth devices of the vehicle owner and the Bluetooth devices of the vehicle owner's family, friends, and pre-registered contacts. Among them, the pre-registered contact person may be a pre-registered courier or property staff.

In the embodiments of the present disclosure, the Bluetooth device may be any mobile device with Bluetooth function. For example, the Bluetooth device may be a mobile phone, a wearable device, or an electronic key. Among them, the wearable device may be a smart bracelet or smart glasses.

In step S12, in response to searching for a Bluetooth device with a preset identification, a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification is established.

In a possible implementation, if the number of Bluetooth devices with preset identification is multiple, in response to searching for any Bluetooth device with preset identification, a Bluetooth pairing between the Bluetooth module and the Bluetooth device with the preset identification is established connection.

In a possible implementation, in response to searching for a Bluetooth device with a preset logo, the Bluetooth module performs identity authentication on the Bluetooth device with the preset logo, and after the identity authentication is passed, the Bluetooth module and the Bluetooth device with the preset logo are established. The Bluetooth pairing connection of the device can thereby improve the security of the Bluetooth pairing connection.

In step S13, in response to the successful Bluetooth pairing connection, wake up and control the image acquisition module installed in the car to acquire the first image of the target object.

In a possible implementation manner, waking up and controlling the image acquisition module installed in the car to collect the first image of the target object includes: awakening the face recognition module installed in the car; control by the awakened face recognition module The image acquisition module acquires the first image of the target object.

In the embodiments of the present disclosure, if a Bluetooth device with a preset identifier is searched, it can indicate to a large extent that a user (such as a car owner) carrying the Bluetooth device with the preset identifier has entered the search range of the Bluetooth module. At this time, by responding to the search for the Bluetooth device with the preset logo, establish a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset logo, and in response to the successful Bluetooth pairing connection, wake up the face recognition module and control the image acquisition module Collecting the first image of the target object, based on the successful Bluetooth pairing connection and then waking up the face recognition module, can effectively reduce the probability of falsely waking up the face recognition module, thereby improving the user experience and effectively reducing the face recognition module. The power consumption of the group. In addition, compared with short-range sensor technologies such as ultrasonic and infrared, the Bluetooth-based pairing connection method has the advantages of high security and support for larger distances. Practice has shown that the time when a user carrying a Bluetooth device with a preset logo reaches the car through this distance (the distance between the user and the car when the Bluetooth pairing connection is successful), and when the car wakes up, the face recognition module switches from a sleep state to a working state When the user arrives at the car door, the face recognition module can be used to recognize the car door immediately without having to wait for the face recognition module to be awakened after the user arrives at the car door. Improve the efficiency of face recognition and improve user experience. In addition, the user has no perception during the Bluetooth pairing and connection process, which can further improve the user experience. Therefore, the embodiments of the present disclosure provide a solution that can better weigh the face recognition module's power saving, user experience, and security by successfully waking up the face recognition module based on the Bluetooth pairing connection.

In a possible implementation, after waking up the face recognition module installed in the car, the method further includes: if the face image is not collected within a preset time, controlling the face recognition module to enter a sleep state . This implementation method controls the face recognition module to enter a sleep state when no face image is collected within a preset time after the face recognition module is awakened, thereby reducing power consumption.

In a possible implementation manner, after waking up the face recognition module installed in the car, the method further includes: if the face recognition fails within a preset time, controlling the face recognition module to enter a sleep state. This implementation method controls the face recognition module to enter the sleep state when the face recognition module fails to pass the face recognition within a preset time after waking up the face recognition module, thereby reducing power consumption.

In step S14, face recognition is performed based on the first image.

In a possible implementation, face recognition includes: living body detection and face authentication; performing face recognition based on the first image includes: collecting the first image through the image sensor in the image acquisition module, and based on the first image. The image and pre-registered facial features are used for face authentication; the first depth map corresponding to the first image is collected by the depth sensor in the image acquisition module, and the living body detection is performed based on the first image and the first depth map.

In the embodiment of the present disclosure, the first image contains the target object. The target object may be a human face or at least a part of a human body, which is not limited in the embodiment of the present disclosure.

Wherein, the first image may be a static image or a video frame image. For example, the first image may be an image selected from a video sequence, where the image may be selected from the video sequence in a variety of ways. In a specific example, the first image is an image selected from a video sequence that meets a preset quality condition, and the preset quality condition may include one or any combination of the following: whether the target object is included, whether the target object is located in the image The center area of the target object, whether the target object is completely contained in the image, the proportion of the target object in the image, the state of the target object (such as the angle of the face), the image clarity, the image exposure, etc., the embodiments of the present disclosure This is not limited.

In one example, the living body detection can be performed first and then the face authentication can be performed. For example, if the live body detection result of the target object is that the target object is a living body, the face authentication process is triggered; if the live body detection result of the target object is that the target object is a prosthesis, the face authentication process is not triggered.

In another example, face authentication can be performed first and then live body detection can be performed. For example, if the face authentication is passed, the living body detection process is triggered; if the face authentication is not passed, the living body detection process is not triggered.

In another example, living body detection and face authentication can be performed at the same time.

In this implementation manner, the living body detection is used to verify whether the target object is a living body, for example, it can be used to verify whether the target object is a human body. Face authentication is used to extract the facial features in the collected images, compare the facial features in the collected images with the pre-registered facial features to determine whether they belong to the same person's facial features, for example, you can determine the collected facial features Whether the facial features in the image belong to the facial features of the vehicle owner.

In the embodiments of the present disclosure, the depth sensor means a sensor for collecting depth information. The embodiments of the present disclosure do not limit the working principle and working band of the depth sensor.

In the embodiments of the present disclosure, the image sensor and the depth sensor of the image acquisition module can be installed separately or together. For example, the image sensor and the depth sensor of the image acquisition module can be set separately, the image sensor adopts RGB (Red, red; Green, green; Blue, blue) sensor or infrared sensor, and the depth sensor adopts binocular infrared sensor or TOF (Time of Flight, time of flight) sensor; the image sensor of the image acquisition module and the depth sensor can be set together. The image acquisition module adopts RGBD (Red, red; Green, green; Blue, blue; Deep, depth) sensor to realize the image sensor And the function of the depth sensor.

As an example, the image sensor is an RGB sensor. If the image sensor is an RGB sensor, the image collected by the image sensor is an RGB image.

As another example, the image sensor is an infrared sensor. If the image sensor is an infrared sensor, the image collected by the image sensor is an infrared image. Among them, the infrared image may be an infrared image with a light spot, or an infrared image without a light spot.

In other examples, the image sensor may be other types of sensors, which is not limited in the embodiment of the present disclosure.

Optionally, the vehicle door unlocking device may obtain the first image in multiple ways. For example, in some embodiments, the vehicle door unlocking device is provided with a camera, and the vehicle door unlocking device uses the camera to collect static images or video streams to obtain the first image, which is not limited in the embodiment of the present disclosure.

As an example, the depth sensor is a three-dimensional sensor. For example, the depth sensor is a binocular infrared sensor, a time-of-flight TOF sensor, or a structured light sensor, where the binocular infrared sensor includes two infrared cameras. The structured light sensor may be a coded structured light sensor or a speckle structured light sensor. By acquiring the depth map of the target object through the depth sensor, a highly accurate depth map can be obtained. In the embodiments of the present disclosure, the depth map containing the target object is used for living body detection, which can fully mine the depth information of the target object, thereby improving the accuracy of living body detection. For example, when the target object is a human face, the embodiment of the present disclosure uses a depth map containing the human face to perform living body detection, which can fully mine the depth information of the face data, thereby improving the accuracy of living body face detection.

In one example, the TOF sensor uses a TOF module based on the infrared band. In this example, by using a TOF module based on the infrared band, the influence of external light on the depth map shooting can be reduced.

In the embodiment of the present disclosure, the first depth map corresponds to the first image. For example, the first depth map and the first image are respectively acquired by the depth sensor and the image sensor for the same scene, or the first depth map and the first image are acquired by the depth sensor and the image sensor for the same target area at the same time , But the embodiment of the present disclosure does not limit this.

Fig. 4a shows a schematic diagram of an image sensor and a depth sensor in a method for unlocking a vehicle door according to an embodiment of the present disclosure. In the example shown in Figure 4a, the image sensor is an RGB sensor, the camera of the image sensor is an RGB camera, and the depth sensor is a binocular infrared sensor. The depth sensor includes two infrared (IR) cameras and two infrared binocular infrared sensors. The cameras are arranged on both sides of the RGB camera of the image sensor. Among them, two infrared cameras collect depth information based on the principle of binocular parallax.

In an example, the image acquisition module further includes at least one fill light, the at least one fill light is arranged between the infrared camera of the binocular infrared sensor and the camera of the image sensor, and the at least one fill light includes At least one of the fill light for the sensor and the fill light for the depth sensor. For example, if the image sensor is an RGB sensor, the fill light used for the image sensor can be a white light; if the image sensor is an infrared sensor, the fill light used for the image sensor can be an infrared light; if the depth sensor is a binocular Infrared sensor, the fill light used for the depth sensor can be an infrared light. In the example shown in FIG. 4a, an infrared lamp is provided between the infrared camera of the binocular infrared sensor and the camera of the image sensor. For example, the infrared lamp can use 940nm infrared.

In one example, the fill light may be in the normally-on mode. In this example, when the camera of the image acquisition module is in the working state, the fill light is in the on state.

In another example, the fill light can be turned on when the light is insufficient. For example, the ambient light intensity can be obtained through the ambient light sensor, and when the ambient light intensity is lower than the light intensity threshold, it is determined that the light is insufficient, and the fill light is turned on.

Fig. 4b shows another schematic diagram of an image sensor and a depth sensor in a method for unlocking a vehicle door according to an embodiment of the present disclosure. In the example shown in FIG. 4b, the image sensor is an RGB sensor, the camera of the image sensor is an RGB camera, and the depth sensor is a TOF sensor.

In an example, the image acquisition module further includes a laser, and the laser is disposed between the camera of the depth sensor and the camera of the image sensor. For example, the laser is arranged between the camera of the TOF sensor and the camera of the RGB sensor. For example, the laser may be a VCSEL (Vertical Cavity Surface Emitting Laser), and the TOF sensor may collect a depth map based on the laser emitted by the VCSEL.

In the embodiments of the present disclosure, the depth sensor is used to collect a depth map, and the image sensor is used to collect a two-dimensional image. It should be noted that although RGB sensors and infrared sensors are used as examples to describe image sensors, and binocular infrared sensors, TOF sensors, and structured light sensors are used as examples to describe depth sensors, those skilled in the art can understand The embodiments of the present disclosure should not be limited to this. Those skilled in the art can select the types of the image sensor and the depth sensor according to actual application requirements, as long as the two-dimensional image and the depth map can be collected respectively.

In step S15, in response to successful face recognition, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.

The vehicle door in the embodiment of the present disclosure may include a vehicle door through which people enter and exit (for example, a left front door, a right front door, a left rear door, and a right rear door), and may also include a trunk door of the vehicle. Correspondingly, the at least one vehicle door lock may include at least one of a left front door lock, a right front door lock, a left rear door lock, a right rear door lock, and a trunk door lock.

In a possible implementation manner, in response to successful face recognition, sending a door unlocking instruction and/or opening a door instruction to at least one door of the vehicle includes: in response to successful face recognition, acquiring at least the vehicle's The state information of a vehicle door; if the state information of the vehicle door is not unlocked, send a door unlock instruction and an open door instruction to the vehicle door; if the state information of the vehicle door is unlocked and not opened, send the vehicle door Send the door open command.

In a possible implementation manner, in response to successful face recognition, sending a door unlock instruction and/or a door opening instruction to at least one door of the vehicle includes: in response to successful face recognition, determining that the target object has a door opening permission ; Send a door unlock instruction and/or open a door instruction to at least one door of the vehicle according to the door for which the target object has the authority to open the door. For example, the doors for which the target object has the authority to open doors may be all doors, or may be trunk doors.

For example, the doors for which the owner or his family or friends have the authority to open doors may be all doors, and the doors for which the courier or property staff has the authority to open doors may be the trunk doors. Among them, the vehicle owner can set the door information for other personnel with the authority to open the door. For another example, in the scene of online car-hailing, the doors for which passengers have the right to open doors may be non-cockpit doors and trunk doors. If the door of the target object with the authority to open the door is a trunk door, the door unlocking instruction can be sent to the trunk door lock.

In an example, if the door of the target object with the permission to open the door only includes the trunk door, it can send the door closing instruction to the trunk door lock after the preset duration of the door unlocking instruction is sent to the trunk door lock, for example, preset The duration can be 3 minutes. For example, if the door that the courier has the right to open includes only the trunk door, he can send the door unlock instruction to the trunk door lock 3 minutes after sending the door close instruction to the trunk door lock, which can satisfy the courier's backup The need for express delivery in the box can improve the safety of the car.

In a possible implementation manner, in addition to determining the door for which the target object has the permission to open the door, the time during which the target object has the permission to open the door, the number of times the permission to open the door corresponding to the target object, etc. may also be determined.

For example, the time when the target object has the right to open the door may be all times, or may be a preset time period. For example, the time when the owner or the owner's family member has the authority to open the door may be all the time. The owner can set the time for other personnel with the authority to open the door. For example, in an application scenario where a friend of a car owner borrows a car from the car owner, the car owner can set the time for the friend to have the permission to open the door to two days. For another example, after the courier contacts the car owner, the car owner can set the time for the courier to open the door to 13:00-14:00 on September 29, 2019. For another example, in a car rental scenario, if a customer rents a car for 3 days, the staff of the car rental agency can set the time for the customer to have the right to open the door to 3 days. For another example, in the online car-hailing scenario, the time when the passenger has the permission to open the door may be the service period of the travel order.

For example, the number of door opening permissions corresponding to the target object may be an unlimited number of times or a limited number of times. For example, the number of door opening permissions corresponding to the car owner or the car owner's family or friends may be unlimited. For another example, the number of door opening permissions corresponding to the courier may be a limited number of times, such as 1 time.

In an example, the SoC of the door unlocking device may send a door unlocking instruction to the door domain controller to control the door to unlock.

In a possible implementation manner, performing live detection based on the first image and the first depth map includes: updating the first depth map based on the first image to obtain the second depth map; based on the first image and the second depth map , To determine the live detection result of the target object.

Specifically, based on the first image, the depth value of one or more pixels in the first depth map is updated to obtain the second depth map.

In some embodiments, based on the first image, the depth value of the depth failure pixel in the first depth map is updated to obtain the second depth map.

Wherein, the depth invalid pixel in the depth map may refer to a pixel with an invalid depth value included in the depth map, that is, a pixel whose depth value is inaccurate or clearly inconsistent with the actual situation. The number of depth failure pixels can be one or more. By updating the depth value of at least one depth failure pixel in the depth map, the depth value of the depth failure pixel is more accurate, which helps to improve the accuracy of living body detection.

In some embodiments, the first depth map is a depth map with missing values, and the second depth map is obtained by repairing the first depth map based on the first image, wherein, optionally, repairing the first depth map includes correcting The depth value of pixels with missing values is determined or supplemented, but the embodiments of the present disclosure are not limited thereto.

In the embodiment of the present disclosure, the first depth map can be updated or repaired in various ways. In some embodiments, the first image is directly used for living body detection, for example, the first image is directly used to update the first depth map. In other embodiments, the first image is preprocessed, and the living body detection is performed based on the preprocessed first image. For example, the image of the target object is acquired from the first image, and the first depth map is updated based on the image of the target object.

The image of the target object can be intercepted from the first image in various ways. As an example, perform target detection on the first image to obtain the location information of the target object, such as the location information of the bounding box of the target object, and intercept the image of the target object from the first image based on the location information of the target object . For example, the image of the area where the bounding box of the target object is intercepted from the first image is taken as the image of the target object, another example is to enlarge the bounding box of the target object by a certain multiple and intercept the area where the enlarged bounding box is located from the first image. The image is the image of the target object. As another example, the key point information of the target object in the first image is acquired, and based on the key point information of the target object, the image of the target object is acquired from the first image.

Optionally, perform target detection on the first image to obtain position information of the area where the target object is located; perform key point detection on the image of the area where the target object is located to obtain key point information of the target object in the first image.

Optionally, the key point information of the target object may include position information of multiple key points of the target object. If the target object is a human face, the key points of the target object may include one or more of eye key points, eyebrow key points, nose key points, mouth key points, and face contour key points. Among them, the eye key points may include one or more of eye contour key points, eye corner key points, and pupil key points.

In an example, the contour of the target object is determined based on the key point information of the target object, and the image of the target object is intercepted from the first image according to the contour of the target object. Compared with the position information of the target object obtained through target detection, the position of the target object obtained through the key point information is more accurate, which is beneficial to improve the accuracy of subsequent living body detection.

Optionally, the contour of the target object in the first image can be determined based on the key points of the target object in the first image, and the image of the area where the contour of the target object in the first image is located or the image of the area obtained after a certain magnification Determine the image of the target object. For example, the elliptical area determined based on the key points of the target object in the first image may be determined as the image of the target object, or the minimum circumscribed rectangular area of the elliptical area determined based on the key points of the target object in the first image may be determined It is the image of the target object, but the embodiment of the present disclosure does not limit this.

In this way, by acquiring the image of the target object from the first image, and performing the living body detection based on the image of the target object, the interference of the background information in the first image on the living body detection can be reduced.

In the embodiments of the present disclosure, the acquired original depth map may be updated, or, in some embodiments, the depth map of the target object is acquired from the first depth map, and the target object’s depth map is updated based on the first image. Depth map to get the second depth map.

As an example, the position information of the target object in the first image is acquired, and based on the position information of the target object, the depth map of the target object is acquired from the first depth map. Optionally, the first depth map and the first image may be registered or aligned in advance, but the embodiment of the present disclosure does not limit this.

In this way, by acquiring the depth map of the target object from the first depth map, and updating the depth map of the target object based on the first image, the second depth map is obtained, which can reduce the background information in the first depth map for living body detection The interference produced.

In some embodiments, after the first image and the first depth map corresponding to the first image are acquired, the first image and the first depth map are aligned according to the parameters of the image sensor and the parameters of the depth sensor.

As an example, conversion processing may be performed on the first depth map, so that the first depth map after the conversion processing is aligned with the first image. For example, the first conversion matrix can be determined according to the parameters of the depth sensor and the parameters of the image sensor, and the first depth map can be converted according to the first conversion matrix. Correspondingly, based on at least a part of the first image, at least a part of the converted first depth map may be updated to obtain a second depth map. For example, based on the first image, the first depth map after the conversion processing is updated to obtain the second depth map. For another example, based on the image of the target object intercepted from the first image, the depth map of the target object intercepted from the first depth map is updated to obtain the second depth map, and so on.

As another example, conversion processing may be performed on the first image, so that the converted first image is aligned with the first depth map. For example, the second conversion matrix can be determined according to the parameters of the depth sensor and the parameters of the image sensor, and the first image can be converted according to the second conversion matrix. Correspondingly, based on at least a part of the converted first image, at least a part of the first depth map may be updated to obtain a second depth map.

Optionally, the parameters of the depth sensor may include internal parameters and/or external parameters of the depth sensor, and the parameters of the image sensor may include internal parameters and/or external parameters of the image sensor. By aligning the first depth map and the first image, the positions of the corresponding parts in the first depth map and the first image can be the same in the two images.

In the above example, the first image is an original image (for example, an RGB or infrared image). In other embodiments, the first image may also refer to an image of a target object intercepted from the original image. Similarly, the first image A depth map may also refer to a depth map of the target object intercepted from the original depth map, which is not limited in the embodiment of the present disclosure.

FIG. 5 shows a schematic diagram of an example of a living body detection method according to an embodiment of the present disclosure. In the example shown in Figure 5, the first image is an RGB image and the target object is a human face. The RGB image and the first depth map are aligned and corrected, and the processed image is input into the face key point model for processing , Get the RGB face map (the image of the target object) and the depth face map (the depth map of the target object), and update or repair the depth face map based on the RGB face map. In this way, the amount of subsequent data processing can be reduced, and the efficiency and accuracy of living body detection can be improved.

In the embodiment of the present disclosure, the live detection result of the target object may be that the target object is a living body or the target object is a prosthesis.

In some embodiments, the first image and the second depth map are input to the living body detection neural network for processing, and the living body detection result of the target object in the first image is obtained. Alternatively, the first image and the second depth map are processed by other living body detection algorithms to obtain the living body detection result.

In some embodiments, feature extraction is performed on the first image to obtain first feature information; feature extraction is performed on the second depth map to obtain second feature information; based on the first feature information and the second feature information, the first feature information is determined The live detection result of the target object in an image.

Optionally, the feature extraction process can be implemented by a neural network or other machine learning algorithms, and the type of feature information extracted can optionally be obtained by learning a sample, which is not limited in the embodiment of the present disclosure.

In some specific scenes (such as outdoor scenes with strong light), the acquired depth map (such as the depth map collected by the depth sensor) may be partially invalid. In addition, under normal light, due to spectacle reflections, black hair, or black spectacle frames, etc., the depth map may randomly cause partial failure of the depth map. And some special paper quality can make the printed face photos produce a similar effect of large-area failure or partial failure of the depth map. In addition, by blocking the active light source of the depth sensor, the depth map can also be partially invalidated, and the imaging of the prosthesis on the image sensor is normal. Therefore, in the case of partial or complete failure of some depth maps, using the depth map to distinguish between the living body and the prosthesis will cause errors. Therefore, in the embodiments of the present disclosure, by repairing or updating the first depth map, and using the repaired or updated depth map for living body detection, it is beneficial to improve the accuracy of living body detection.

In this example, the first image and the second depth map are input into the living body detection network for living body detection processing, and the living body detection result is obtained.

As shown in Figure 6, the living body detection network includes two branches, namely a first sub-network and a second sub-network. The first sub-network is used to perform feature extraction processing on the first image to obtain first feature information. The two sub-networks are used to perform feature extraction processing on the second depth map to obtain second feature information.

In an optional example, the first sub-network may include a convolutional layer, a downsampling layer, and a fully connected layer.

For example, the first sub-network may include a first-level convolutional layer, a first-level down-sampling layer, and a first-level fully connected layer. Wherein, the level of convolutional layer may include one or more convolutional layers, the level of downsampling layer may include one or more downsampling layers, and the level of fully connected layer may include one or more fully connected layers.

For another example, the first sub-network may include a multi-level convolutional layer, a multi-level down-sampling layer, and a first-level fully connected layer. Wherein, each level of convolutional layer may include one or more convolutional layers, each level of downsampling layer may include one or more downsampling layers, and this level of fully connected layer may include one or more fully connected layers. Among them, the i-th convolutional layer is cascaded after the i-th down-sampling layer, the i-th down-sampling layer is cascaded after the i+1-th convolutional layer, and the n-th down-sampling layer is cascaded after the fully connected layer, where , I and n are both positive integers, 1≤i≤n, n represents the number of convolutional layers and downsampling layers in the depth prediction neural network.

Alternatively, the first sub-network may include a convolutional layer, a down-sampling layer, a normalization layer, and a fully connected layer.

For example, the first sub-network may include a first-level convolutional layer, a normalization layer, a first-level down-sampling layer, and a first-level fully connected layer. Wherein, the level of convolutional layer may include one or more convolutional layers, the level of downsampling layer may include one or more downsampling layers, and the level of fully connected layer may include one or more fully connected layers.

For another example, the first sub-network may include a multi-level convolutional layer, a plurality of normalization layers, a multi-level down-sampling layer, and a first-level fully connected layer. Wherein, each level of convolutional layer may include one or more convolutional layers, each level of downsampling layer may include one or more downsampling layers, and this level of fully connected layer may include one or more fully connected layers. Among them, the i-th normalized layer is cascaded after the i-th convolutional layer, the i-th downsampling layer is cascaded after the i-th normalized layer, and the i+1-th level is cascaded after the i-th down-sampling layer Convolutional layer, cascaded fully connected layer after the nth downsampling layer, where i and n are both positive integers, 1≤i≤n, and n represents the number of convolutional and downsampling layers in the first sub-network And the number of normalization layers.

As an example, perform convolution processing on the first image to obtain the first convolution result; perform down-sampling processing on the first convolution result to obtain the first down-sampling result; and obtain the first feature information based on the first down-sampling result .

For example, the first image may be subjected to convolution processing and down-sampling processing through a first-level convolution layer and a first-level down-sampling layer. Wherein, the level of convolutional layer may include one or more convolutional layers, and the level of downsampling layer may include one or more downsampling layers.

For another example, the first image may be subjected to convolution processing and down-sampling processing through a multi-level convolution layer and a multi-level down-sampling layer. Wherein, each level of convolutional layer may include one or more convolutional layers, and each level of downsampling layer may include one or more downsampling layers.

For example, performing down-sampling processing on the first convolution result to obtain the first down-sampling result may include: performing normalization processing on the first convolution result to obtain the first normalization result; and performing the first normalization result Perform down-sampling processing to obtain the first down-sampling result.

For example, the first down-sampling result may be input to the fully connected layer, and the first down-sampling result may be fused through the fully connected layer to obtain the first characteristic information.

Optionally, the second sub-network and the first sub-network have the same network structure, but have different parameters. Alternatively, the second sub-network has a different network structure from the first sub-network, which is not limited in the embodiment of the present disclosure.

As shown in Figure 6, the living body detection network also includes a third sub-network for processing the first feature information obtained by the first sub-network and the second feature information obtained by the second sub-network to obtain the target in the first image. The result of the live test of the subject. Optionally, the third sub-network may include a fully connected layer and an output layer. For example, the output layer adopts the softmax function. If the output of the output layer is 1, it means that the target object is a living body, and if the output of the output layer is 0, it means that the target object is a prosthesis. The specific implementation is not limited.

As an example, the first feature information and the second feature information are fused to obtain the third feature information; based on the third feature information, the live detection result of the target object in the first image is determined.

For example, the first feature information and the second feature information are fused through the fully connected layer to obtain the third feature information.

In some embodiments, based on the third feature information, the probability that the target object in the first image is a living body is obtained, and the living body detection result of the target object is determined according to the probability that the target object is a living body.

For example, if the probability that the target object is a living body is greater than the second threshold, it is determined that the target object's living body detection result is that the target object is a living body. For another example, if the probability that the target object is a living body is less than or equal to the second threshold, it is determined that the living body detection result of the target object is a prosthesis.

In other embodiments, based on the third characteristic information, the probability that the target object is a prosthesis is obtained, and the live detection result of the target object is determined according to the probability that the target object is the prosthesis. For example, if the probability that the target object is a prosthesis is greater than the third threshold, it is determined that the target object's live body detection result is that the target object is a prosthesis. For another example, if the probability that the target object is a prosthesis is less than or equal to the third threshold, it is determined that the live body detection result of the target object is a live body.

In an example, the third feature information can be input into the Softmax layer, and the probability that the target object is a living body or a prosthesis can be obtained through the Softmax layer. For example, the output of the Softmax layer includes two neurons, where one neuron represents the probability that the target object is a living body, and the other neuron represents the probability that the target object is a prosthesis, but the embodiments of the present disclosure are not limited thereto.

In the embodiment of the present disclosure, by acquiring the first image and the first depth map corresponding to the first image, based on the first image, updating the first depth map to obtain the second depth map, based on the first image and the second depth map, The live detection result of the target object in the first image is determined, so that the depth map can be perfected, thereby improving the accuracy of the live detection.

In a possible implementation manner, updating the first depth map based on the first image to obtain the second depth map includes: determining depth prediction values and associated information of multiple pixels in the first image based on the first image, where , The association information of the plurality of pixels indicates the degree of association between the plurality of pixels; based on the depth prediction value and the association information of the plurality of pixels, the first depth map is updated to obtain the second depth map.

Specifically, the depth prediction values of multiple pixels in the first image are determined based on the first image, and the first depth map is repaired and perfected based on the depth prediction values of the multiple pixels.

Specifically, by processing the first image, the depth prediction values of multiple pixels in the first image are obtained. For example, the first image is input into the depth prediction depth network for processing to obtain the depth prediction results of multiple pixels, for example, the depth prediction map corresponding to the first image is obtained, but the embodiment of the present disclosure does not limit this.

In some embodiments, based on the first image and the first depth map, the depth prediction values of multiple pixels in the first image are determined.

As an example, the first image and the first depth map are input to the depth prediction neural network for processing to obtain depth prediction values of multiple pixels in the first image. Alternatively, the first image and the first depth map are processed in other ways to obtain depth prediction values of multiple pixels, which is not limited in the embodiment of the present disclosure.

Fig. 7 shows a schematic diagram of a depth prediction neural network in a method for unlocking a vehicle door according to an embodiment of the present disclosure. As shown in FIG. 7, the first image and the first depth map can be input to the depth prediction neural network for processing to obtain an initial depth estimation map. Based on the initial depth estimation map, the depth prediction values of multiple pixels in the first image can be determined. For example, the pixel value of the initial depth estimation map is the depth prediction value of the corresponding pixel in the first image.

The deep prediction neural network can be realized through a variety of network structures. In one example, the depth prediction neural network includes an encoding part and a decoding part. Wherein, optionally, the encoding part may include a convolutional layer and a downsampling layer, and the decoding part may include a deconvolutional layer and/or an upsampling layer. In addition, the encoding part and/or the decoding part may further include a normalization layer, and the embodiment of the present disclosure does not limit the specific implementation of the encoding part and the decoding part. In the encoding part, as the number of network layers increases, the resolution of the feature map gradually decreases, and the number of feature maps gradually increases, so that rich semantic features and image spatial features can be obtained; in the decoding part, the resolution of the feature map gradually increases Large, the resolution of the feature map finally output by the decoding part is the same as the resolution of the first depth map.

In some embodiments, fusion processing is performed on the first image and the first depth map to obtain a fusion result, and based on the fusion result, the depth prediction values of multiple pixels in the first image are determined.

In an example, the first image and the first depth map can be concat to obtain the fusion result.

In one example, convolution processing is performed on the fusion result to obtain the second convolution result; down-sampling processing is performed based on the second convolution result to obtain the first encoding result; based on the first encoding result, multiple images in the first image are determined The predicted depth value of the pixel.

For example, convolution processing may be performed on the fusion result through the convolution layer to obtain the second convolution result.

For example, normalization processing is performed on the second convolution result to obtain the second normalization result; down sampling processing is performed on the second normalization result to obtain the first encoding result. Here, the second normalized result can be normalized by the normalization layer to obtain the second normalized result; the second normalized result can be down-sampled by the down-sampling layer to obtain the first encoding result . Alternatively, the second convolution result may be down-sampled through the down-sampling layer to obtain the first encoding result.

For example, perform deconvolution processing on the first encoding result to obtain the first deconvolution result; perform normalization processing on the first deconvolution result to obtain the depth prediction value. Here, the first encoding result can be deconvolved by the deconvolution layer to obtain the first deconvolution result; the first deconvolution result can be normalized by the normalization layer to obtain the depth prediction value . Alternatively, the first encoding result may be deconvolved through the deconvolution layer to obtain the depth prediction value.

For example, performing up-sampling processing on the first encoding result to obtain the first up-sampling result; performing normalization processing on the first up-sampling result to obtain the depth prediction value. Here, the up-sampling process may be performed on the first encoding result through the up-sampling layer to obtain the first up-sampling result; the first up-sampling result may be normalized through the normalization layer to obtain the depth prediction value. Alternatively, the upsampling process may be performed on the first encoding result through the upsampling layer to obtain the depth prediction value.

In addition, by processing the first image, the associated information of multiple pixels in the first image is obtained. Wherein, the association information of the plurality of pixels in the first image may include the degree of association between each pixel in the plurality of pixels of the first image and its surrounding pixels. Wherein, the surrounding pixels of the pixel may include at least one adjacent pixel of the pixel, or include a plurality of pixels that are separated from the pixel by no more than a certain value. For example, as shown in FIG. 10, the surrounding pixels of pixel 5 include pixels 1, pixel 2, pixel 3, pixel 4, pixel 6, pixel 7, pixel 8, and pixel 9 adjacent to it. Accordingly, there are more pixels in the first image. The associated information of each pixel includes pixel 1, pixel 2, pixel 3, pixel 4, pixel 6, pixel 7, pixel 8, and the degree of association between pixel 9 and pixel 5. As an example, the degree of association between the first pixel and the second pixel may be measured by the correlation between the first pixel and the second pixel. The embodiments of the present disclosure may use related technologies to determine the correlation between pixels. This will not be repeated here.

In the embodiments of the present disclosure, the associated information of multiple pixels can be determined in various ways. In some embodiments, the first image is input to the correlation detection neural network for processing to obtain correlation information of multiple pixels in the first image. For example, the associated feature map corresponding to the first image is obtained. Alternatively, other algorithms may be used to obtain the associated information of multiple pixels, which is not limited in the embodiment of the present disclosure.

Fig. 8 shows a schematic diagram of a correlation detection neural network in a method for unlocking a vehicle door according to an embodiment of the present disclosure. As shown in Figure 8, the first image is input to the correlation detection neural network for processing, and multiple correlation feature maps are obtained. Based on multiple associated feature maps, the associated information of multiple pixels in the first image can be determined. For example, the surrounding pixels of a certain pixel refer to the pixels whose distance from the pixel is equal to 0, that is, the surrounding pixels of the pixel refer to the pixels adjacent to the pixel, the correlation detection neural network can output 8 correlations Feature map. For example, in the first associated feature map, the pixel value of the pixel Pi _,j = the degree of correlation between the pixel Pi _-1,j-1 and the pixel Pi _,j in the first image, where Pi _{, j} represents the pixel in the i-th row and j-th column; in the second correlation feature map, the pixel value of the pixel Pi _,j = the correlation between the pixel Pi _-1,j and the pixel Pi _,j in the first image Degree; in the third associated feature map, the pixel value of the pixel Pi _,j = the degree of association between the pixel Pi _-1,j+1 and the pixel Pi _,j in the first image; in the fourth image FIG feature, the correlation between the pixel P _{i, j} of the pixel values of the first image pixel = P _{i, j-1} and the pixel P _{i, j;} in FIG fifth related feature, the pixel P _{i, j} The pixel value of = the correlation degree between the pixel Pi _,j+1 and the pixel Pi _,j in the first image; in the sixth associated feature map _, the pixel value of the pixel Pi _,j = the pixel in the first image The degree of association between _Pi+1,j-1 and pixels Pi _,j ; in the seventh associated feature map _, the pixel value of pixels Pi _,j =pixels Pi _{+1,j in the} first image and The degree of correlation between pixels Pi _,j ; in the eighth associated feature map, the pixel value of pixel Pi _,j =between pixel Pi _+1,j+1 and pixel Pi _{,j in the} first image The degree of relevance.

The correlation detection neural network can be realized through a variety of network structures. As an example, the correlation detection neural network may include an encoding part and a decoding part. The coding part may include a convolutional layer and a downsampling layer, and the decoding part may include a deconvolutional layer and/or an upsampling layer. The encoding part may also include a normalization layer, and the decoding part may also include a normalization layer. In the coding part, the resolution of the feature map gradually decreases, and the number of feature maps gradually increases, so as to obtain rich semantic features and image spatial features; in the decoding part, the resolution of the feature map gradually increases, and the final output feature map of the decoding part The resolution is the same as the resolution of the first image. In the embodiment of the present disclosure, the associated information may be an image, or may be other data forms, such as a matrix.

As an example, inputting the first image into the correlation detection neural network for processing to obtain correlation information of multiple pixels in the first image may include: performing convolution processing on the first image to obtain a third convolution result; The third convolution result is subjected to down-sampling processing to obtain the second encoding result; based on the second encoding result, the associated information of multiple pixels in the first image is obtained.

In an example, the first image may be convolved through the convolution layer to obtain the third convolution result.

In one example, performing down-sampling processing based on the third convolution result to obtain the second encoding result may include: normalizing the third convolution result to obtain the third normalization result; normalizing the third The transformation result is subjected to down-sampling processing to obtain the second encoding result. In this example, the third convolution result can be normalized by the normalization layer to obtain the third normalized result; the third normalized result can be downsampled by the downsampling layer to obtain the second Encoding results. Alternatively, the third convolution result may be down-sampled through the down-sampling layer to obtain the second encoding result.

In one example, determining the associated information based on the second encoding result may include: performing deconvolution processing on the second encoding result to obtain a second deconvolution result; performing normalization processing on the second deconvolution result, Get related information. In this example, the second encoding result can be deconvolved through the deconvolution layer to obtain the second deconvolution result; the second deconvolution result can be normalized through the normalization layer to obtain the correlation information. Alternatively, the second encoding result may be deconvolved through the deconvolution layer to obtain the associated information.

In one example, determining the associated information based on the second encoding result may include: performing up-sampling processing on the second encoding result to obtain the second up-sampling result; normalizing the second up-sampling result to obtain the associated information . In an example, the second encoding result may be up-sampled through the up-sampling layer to obtain the second up-sampling result; the second up-sampling result may be normalized through the normalization layer to obtain the associated information. Alternatively, the second encoding result may be up-sampled through the up-sampling layer to obtain the associated information.

Current 3D sensors such as TOF and structured light are easily affected by sunlight outdoors, resulting in a large area of voids in the depth map, which affects the performance of the 3D live detection algorithm. The 3D living body detection algorithm based on the self-improvement of the depth map proposed in the embodiments of the present disclosure improves the performance of the 3D living body detection algorithm by perfecting and repairing the depth map detected by the 3D sensor.

In some embodiments, after obtaining the depth prediction values and associated information of multiple pixels, the first depth map is updated based on the depth prediction values and associated information of the multiple pixels to obtain the second depth map. Fig. 9 shows an exemplary schematic diagram of updating the depth map in a method for unlocking a vehicle door according to an embodiment of the present disclosure. In the example shown in Figure 9, the first depth map is a depth map with missing values, and the obtained depth prediction values and associated information of multiple pixels are the initial depth estimation map and the associated feature map. At this time, there will be missing values. The value depth map, the initial depth estimation map, and the associated feature map are input to the depth map update module (for example, the depth update neural network) for processing to obtain the final depth map, that is, the second depth map.

In some embodiments, the depth prediction value of the depth failure pixel and the depth prediction value of multiple surrounding pixels of the depth failure pixel are obtained from the depth prediction values of the plurality of pixels; the depth failure value is obtained from the associated information of the plurality of pixels The correlation between the pixel and the multiple surrounding pixels of the depth-failed pixel; based on the depth prediction value of the depth-failed pixel, the depth prediction value of the multiple surrounding pixels of the depth-failed pixel, and the relationship between the depth-failed pixel and the surrounding pixels of the depth-failed pixel The correlation degree between the two determines the updated depth value of the depth failure pixel.

In the embodiments of the present disclosure, the depth invalid pixels in the depth map can be determined in various ways. As an example, a pixel with a depth value equal to 0 in the first depth map is determined as a depth failure pixel, or a pixel in the first depth map without a depth value is determined as a depth failure pixel.

In this example, for the value part of the first depth map with missing values (that is, the depth value is not 0), we believe that the depth value is correct and credible, and this part is not updated and the original depth is retained value. The depth value of the pixel whose depth value is 0 in the first depth map is updated.

As another example, the depth sensor may set the depth value of the depth failure pixel to one or more preset values or preset ranges. In an example, pixels whose depth values in the first depth map are equal to a preset value or belonging to a preset range may be determined as depth-invalidated pixels.

The embodiment of the present disclosure may also determine the depth failure pixel in the first depth map based on other statistical methods, which is not limited in the embodiment of the present disclosure.

In this implementation manner, the depth value of the pixel in the first image with the same position as the depth failure pixel can be determined as the depth prediction value of the depth failure pixel. Similarly, the surrounding pixel positions of the depth failure pixel in the first image can be determined. The depth value of the same pixel is determined as the depth prediction value of the surrounding pixels of the depth failure pixel.

As an example, the distance between the surrounding pixels of the depth failure pixel and the depth failure pixel is less than or equal to the first threshold.

FIG. 10 shows a schematic diagram of surrounding pixels in a method for unlocking a vehicle door according to an embodiment of the present disclosure. For example, if the first threshold is 0, only neighbor pixels are used as surrounding pixels. For example, if the neighboring pixels of pixel 5 include pixel 1, pixel 2, pixel 3, pixel 4, pixel 6, pixel 7, pixel 8, and pixel 9, then only pixel 1, pixel 2, pixel 3, pixel 4, pixel 6, Pixel 7, pixel 8, and pixel 9 serve as surrounding pixels of pixel 5.

FIG. 11 shows another schematic diagram of surrounding pixels in a method for unlocking a vehicle door according to an embodiment of the present disclosure. For example, if the first threshold is 1, in addition to using neighbor pixels as surrounding pixels, neighbor pixels of neighbor pixels are also used as surrounding pixels. That is, in addition to pixels 1, pixel 2, pixel 3, pixel 4, pixel 6, pixel 7, pixel 8, and pixel 9 as surrounding pixels of pixel 5, pixels 10 to 25 are used as surrounding pixels of pixel 5.

As an example, based on the depth prediction value of the surrounding pixels of the depth failure pixel and the correlation between the depth failure pixel and multiple surrounding pixels of the depth failure pixel, the depth correlation value of the depth failure pixel is determined; depth prediction based on the depth failure pixel The value and the depth associated value determine the updated depth value of the depth failure pixel.

As another example, based on the depth prediction value of the surrounding pixels of the depth failing pixel and the correlation between the depth failing pixel and the surrounding pixels, determine the effective depth value of the surrounding pixel for the depth failing pixel; based on each surrounding of the depth failing pixel The effective depth value of the pixel for the depth failure pixel and the depth prediction value of the depth failure pixel determine the updated depth value of the depth failure pixel. For example, the product of the depth prediction value of a certain surrounding pixel of the depth failure pixel and the correlation degree corresponding to the surrounding pixel can be determined as the effective depth value of the surrounding pixel for the depth failure pixel, where the correlation degree corresponding to the surrounding pixel It refers to the degree of correlation between the surrounding pixels and the depth failure pixels. For example, it is possible to determine the product of the sum of the effective depth values of the depth-failed pixels for the depth-failed pixels and the first preset coefficient to obtain the first product; determine the depth prediction value of the depth-failed pixels and the second preset coefficient The product is multiplied to obtain the second product; the sum of the first product and the second product is determined as the updated depth value of the depth failure pixel. In some embodiments, the sum of the first preset coefficient and the second preset coefficient is 1.

In one example, the degree of association between the depth failure pixel and each surrounding pixel is used as the weight of each surrounding pixel, and the depth prediction values of multiple surrounding pixels of the depth failure pixel are weighted and summed to obtain the depth failure pixel The depth of the correlation value. For example, if pixel 5 is a depth failure pixel, the depth correlation value of depth failure pixel 5 is

And formula 1 can be used to determine the updated depth value F ₅ ′ of the depth failure pixel 5,

among them,

W _i represents the correlation between the pixel i and the pixel 5, F _i represents the depth of the prediction value of pixel i.

In another example, the product of the correlation between each surrounding pixel and the depth failing pixel in the multiple surrounding pixels of the depth failure pixel and the depth prediction value of each surrounding pixel is determined; the maximum value of the product is determined as the depth failure The depth associated value of the pixel.

In one example, the sum of the depth prediction value of the depth failure pixel and the depth associated value is determined as the updated depth value of the depth failure pixel.

In another example, the product of the depth prediction value of the depth failure pixel and the third preset coefficient is determined to obtain the third product; the product of the depth correlation value and the fourth preset coefficient is determined to obtain the fourth product; and the third product is multiplied by The sum of the fourth product is determined as the updated depth value of the depth failure pixel. In some embodiments, the sum of the third preset coefficient and the fourth preset coefficient is 1.

In some embodiments, the depth value of the non-depth failure pixel in the second depth map is equal to the depth value of the non-depth failure pixel in the first depth map.

In other embodiments, the depth value of the non-depth failure pixel may also be updated to obtain a more accurate second depth map, which can further improve the accuracy of the living body detection.

In the embodiment of the present disclosure, the Bluetooth module provided in the car searches for the Bluetooth device with the preset identification, and in response to the search for the Bluetooth device with the preset identification, the Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification is established, in response to The Bluetooth pairing connection is successful, wake up and control the image acquisition module installed in the car to collect the first image of the target object, perform face recognition based on the first image, and send the door unlock to at least one door of the car in response to the successful face recognition Commands and/or open door commands, so that when a Bluetooth pairing connection is not established with a Bluetooth device with a preset logo, the face recognition module can be in a dormant state to maintain low-power operation, thereby reducing the amount of face recognition and opening the door. Operating power consumption, and can make the face recognition module in a working state before the user of the Bluetooth device with the preset logo arrives at the car door, and the image acquisition module collects when the user of the Bluetooth device with the preset logo arrives at the door After the first image is reached, the face image processing can be quickly performed through the awakened face recognition module, thereby improving the efficiency of face recognition and improving user experience. Therefore, the embodiments of the present disclosure can not only meet the requirements of low-power operation, but also meet the requirements of fast opening doors. With the embodiments of the present disclosure, when the owner approaches the vehicle, without deliberately making actions (such as touching a button or making gestures), the living body detection and face authentication process can be automatically triggered, and it is automatically turned on after the owner passes the living body detection and face authentication. Car door.

In a possible implementation manner, after performing face recognition based on the first image, the method further includes: in response to a face recognition failure, activating a password unlocking module provided in the car to start a password unlocking process.

In this implementation, password unlocking is an alternative to face recognition unlocking. The reasons for the failure of face recognition may include at least one of the result of the living body detection being that the target object is a prosthesis, the face authentication failure, the failure of image collection (for example, a camera failure), and the number of recognition times exceeding a predetermined number. When the target object does not pass face recognition, the password unlocking process is initiated. For example, the password entered by the user can be obtained through the touch screen on the B-pillar. In one example, after entering the wrong password M times in succession, the password unlocking will become invalid, for example, M is equal to 5.

In a possible implementation, the method further includes one or both of the following: performing vehicle owner registration based on the facial image of the vehicle owner collected by the image acquisition module; performing remotely based on the facial image of the vehicle owner collected by the vehicle owner’s terminal device Register and send the registration information to the car, where the registration information includes the face image of the car owner.

In one example, the registration of the car owner based on the face image of the car owner collected by the image acquisition module includes: when the registration button on the touch screen is detected to be clicked, the user is requested to enter a password, and after the password verification is passed, the image acquisition module is started. The RGB cameras in the group acquire the user’s face image, and register according to the acquired face image, and extract the facial features in the face image as pre-registered facial features to be based on the pre-registered facial features during subsequent face authentication. Compare the registered face features.

In an example, remote registration is performed according to the face image of the vehicle owner collected by the terminal device of the vehicle owner, and the registration information is sent to the vehicle, where the registration information includes the face image of the vehicle owner. In this example, the car owner can send a registration request to the TSP (Telematics Service Provider) cloud through the mobile phone App (Application), where the registration request can carry the face image of the car owner; the TSP cloud sends the registration request Send to the vehicle-mounted T-Box (Telematics Box, telematics processor) of the door unlocking device. The vehicle-mounted T-Box activates the face recognition function according to the registration request, and uses the facial features in the face image carried in the registration request as the pre- The registered facial features are compared based on the pre-registered facial features in subsequent face authentication.

FIG. 12 shows another flowchart of a method for unlocking a vehicle door according to an embodiment of the present disclosure. The vehicle door unlocking method may be executed by a vehicle door unlocking device. In a possible implementation manner, the method for unlocking the vehicle door may be implemented by a processor calling a computer readable instruction stored in the memory. For the sake of brevity, the similar parts as above will not be repeated below. As shown in FIG. 12, the method for unlocking the vehicle door includes steps S21 to S24.

In step S21, the Bluetooth module installed in the car searches for a Bluetooth device with a preset identification.

In a possible implementation manner, the search for a Bluetooth device with a preset identifier via the Bluetooth module provided in the car includes: when the car is in a stalled state or in a stalled state with the door locked, The Bluetooth module of the car searches for the Bluetooth device with preset identification.

In step S22, in response to searching for the Bluetooth device with the preset identifier, wake up and control the image acquisition module provided in the vehicle to acquire the first image of the target object.

In a possible implementation manner, the number of Bluetooth devices with the preset identification is one.

In another possible implementation manner, the number of Bluetooth devices with the preset identification is multiple; and in response to searching for the Bluetooth device with the preset identification, wake up and control the image collection set in the car The module collecting the first image of the target object includes: in response to searching for any Bluetooth device with a preset identification, waking up and controlling the image collecting module installed in the vehicle to collect the first image of the target object.

In a possible implementation manner, the awakening and controlling the image acquisition module installed in the car to collect the first image of the target object includes: awakening the face recognition module installed in the car; The face recognition module controls the image acquisition module to acquire the first image of the target object.

Compared with short-distance sensor technologies such as ultrasonic and infrared, the embodiments of the present disclosure can support a larger distance by adopting Bluetooth. Practice shows that the time when a user carrying a Bluetooth device with a preset logo passes through this distance (the distance between the user and the car when the Bluetooth module of the car searches for the Bluetooth device with the user's preset logo), and the car wakes up the face The time for the recognition module to switch from the sleep state to the working state roughly matches, so that when the user arrives at the door, the face recognition module can be used to recognize the door immediately without having to wait after the user arrives at the door The face recognition module is awakened, which can increase the efficiency of face recognition and improve user experience. In addition, the user has no perception during the Bluetooth search process, which can further improve the user experience. Therefore, the embodiments of the present disclosure provide a way of waking up the face recognition module in response to searching for the Bluetooth device with the preset identification, which can better weigh the face recognition module power saving, user experience, and security. Aspects of the solution.

In a possible implementation, after waking up the face recognition module installed in the car, the method further includes: if no face image is collected within a preset time, controlling the person The face recognition module enters a sleep state.

In a possible implementation, after waking up the face recognition module installed in the car, the method further includes: if the face recognition fails within a preset time, controlling the face The recognition module enters a sleep state.

In step S23, face recognition is performed based on the first image.

In step S24, in response to successful face recognition, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.

In a possible implementation manner, in response to successful face recognition, sending a door unlocking instruction and/or opening a door instruction to at least one door of the vehicle includes: determining the target in response to successful face recognition The door for which the object has the authority to open the door; according to the door for which the target object has the authority to open the door, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.

In a possible implementation manner, the face recognition includes: living body detection and face authentication; the performing face recognition based on the first image includes: collecting by an image sensor in the image acquisition module The first image, and perform face authentication based on the first image and pre-registered facial features; collect the first depth map corresponding to the first image through the depth sensor in the image acquisition module, and Performing living body detection based on the first image and the first depth map.

In a possible implementation manner, the performing living body detection based on the first image and the first depth map includes: updating the first depth map based on the first image to obtain a second depth map ; Based on the first image and the second depth map, determine the live detection result of the target object.

In a possible implementation manner, the image sensor includes an RGB image sensor or an infrared sensor; the depth sensor includes a binocular infrared sensor or a time-of-flight TOF sensor.

In a possible implementation manner, the TOF sensor adopts a TOF module based on an infrared band.

In a possible implementation manner, the updating the first depth map based on the first image to obtain the second depth map includes: comparing the data in the first depth map based on the first image The depth value of the depth failure pixel is updated to obtain the second depth map.

In a possible implementation manner, the updating the first depth map based on the first image to obtain the second depth map includes: determining a plurality of the first images based on the first image The depth prediction value and associated information of the pixel, wherein the associated information of the plurality of pixels indicates the degree of association between the plurality of pixels; based on the depth prediction value and the associated information of the plurality of pixels, the first Depth map to get the second depth map.

In a possible implementation manner, the updating the first depth map based on the depth prediction values and associated information of the plurality of pixels to obtain a second depth map includes: determining the value in the first depth map Depth failure pixel; obtaining the depth prediction value of the depth failure pixel and the depth prediction value of a plurality of surrounding pixels of the depth failure pixel from the depth prediction values of the plurality of pixels; from the associated information of the plurality of pixels Obtaining the correlation between the depth failure pixel and multiple surrounding pixels of the depth failure pixel; based on the depth prediction value of the depth failure pixel, the depth prediction value of the multiple surrounding pixels of the depth failure pixel, And the degree of association between the depth failure pixel and surrounding pixels of the depth failure pixel to determine the updated depth value of the depth failure pixel.

In a possible implementation, the depth prediction value based on the depth failure pixel, the depth prediction values of multiple surrounding pixels of the depth failure pixel, and the difference between the depth failure pixel and the depth failure pixel Determining the updated depth value of the depth failure pixel based on the correlation between multiple surrounding pixels, including: the depth prediction value of the surrounding pixels based on the depth failure pixel and the depth failure pixel and the depth failure pixel Determine the depth correlation value of the depth failure pixel; determine the updated depth of the depth failure pixel based on the depth prediction value of the depth failure pixel and the depth correlation value value.

In a possible implementation manner, the determining the depth prediction value based on the depth prediction value of the surrounding pixels of the depth failure pixel and the correlation between the depth failure pixel and the multiple surrounding pixels of the depth failure pixel The depth correlation value of the depth failure pixel includes: using the correlation degree between the depth failure pixel and each surrounding pixel as the weight of each surrounding pixel, and predicting the depth of multiple surrounding pixels of the depth failure pixel The value is weighted and summed to obtain the depth associated value of the depth failure pixel.

In a possible implementation manner, the determining depth prediction values of multiple pixels in the first image based on the first image includes: determining based on the first image and the first depth map Depth prediction values of multiple pixels in the first image.

In a possible implementation manner, the determining the depth prediction values of multiple pixels in the first image based on the first image and the first depth map includes: combining the first image and the The first depth map is input to a depth prediction neural network for processing to obtain depth prediction values of multiple pixels in the first image.

In a possible implementation manner, the determining the depth prediction values of multiple pixels in the first image based on the first image and the first depth map includes: comparing the first image and the first depth map. The first depth map is subjected to fusion processing to obtain a fusion result; based on the fusion result, the depth prediction values of multiple pixels in the first image are determined.

In a possible implementation manner, the determining the association information of multiple pixels in the first image based on the first image includes: inputting the first image to a correlation detection neural network for processing, Obtain the associated information of multiple pixels in the first image.

In a possible implementation manner, the updating the first depth map based on the first image includes: acquiring an image of the target object from the first image; and based on the image of the target object , Update the first depth map.

In a possible implementation manner, the obtaining an image of the target object from the first image includes: obtaining key point information of the target object in the first image; The key point information is to obtain the image of the target object from the first image.

In a possible implementation manner, the acquiring key point information of the target object in the first image includes: performing target detection on the first image to obtain the area where the target object is located; The image of the area where the target object is located performs key point detection to obtain the key point information of the target object in the first image.

In a possible implementation manner, the updating the first depth map based on the first image to obtain a second depth map includes: obtaining a depth map of the target object from the first depth map ; Based on the first image, update the depth map of the target object to obtain the second depth map.

In a possible implementation manner, the determining the live detection result of the target object based on the first image and the second depth map includes: combining the first image and the second depth map Input to the living body detection neural network for processing, and obtain the living body detection result of the target object.

In a possible implementation manner, the determining the live detection result of the target object based on the first image and the second depth map includes: performing feature extraction processing on the first image to obtain the first image One feature information; performing feature extraction processing on the second depth map to obtain second feature information; and determining the live detection result of the target object based on the first feature information and the second feature information.

In a possible implementation, the determining the live detection result of the target object based on the first feature information and the second feature information includes: comparing the first feature information and the second feature information The feature information is fused to obtain third feature information; based on the third feature information, the live detection result of the target object is determined.

In a possible implementation manner, the determining the live detection result of the target object based on the third characteristic information includes: obtaining the probability that the target object is alive based on the third characteristic information; The probability that the target object is a living body determines the result of the living body detection of the target object.

In a possible implementation, after the face recognition is performed based on the first image, the method further includes: in response to the face recognition failure, activating a password unlocking module provided in the car to activate the password Unlocking process.

In a possible implementation, the method further includes one or both of the following: performing vehicle owner registration based on the face image of the vehicle owner collected by the image acquisition module; and performing vehicle owner registration based on the vehicle owner’s terminal device collected The face image of the vehicle owner is remotely registered and the registration information is sent to the vehicle, where the registration information includes the face image of the vehicle owner.

It can be understood that, without violating the principle logic, the various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment, which is limited in length and will not be repeated in this disclosure.

Those skilled in the art can understand that in the above methods of the specific implementation, the writing order of the steps does not mean a strict execution order but constitutes any limitation on the implementation process. The specific execution order of each step should be based on its function and possibility. The inner logic is determined.

In addition, the present disclosure also provides a vehicle door unlocking device, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any of the vehicle door unlocking methods provided in the present disclosure. The corresponding technical solutions and descriptions and the corresponding records in the method section ,No longer.

FIG. 13 shows a block diagram of a vehicle door unlocking device according to an embodiment of the present disclosure. As shown in FIG. 13, the vehicle door unlocking device includes: a search module 31, which is used to search for a Bluetooth device with a preset identification via a Bluetooth module provided in the car; The Bluetooth device establishes a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification, and in response to the successful Bluetooth pairing connection, wakes up and controls the image acquisition module set in the car to collect the second target object An image, or, in response to searching for the Bluetooth device with the preset identification, wake up and control the image acquisition module installed in the car to collect the first image of the target object; the face recognition module 33 is used to The first image performs face recognition; the unlocking module 34 is configured to send a door unlocking instruction and/or a door opening instruction to at least one door of the vehicle in response to a successful face recognition.

In the embodiment of the present disclosure, the Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification is established in response to searching for the Bluetooth device with the preset identification, and in response to the successful Bluetooth pairing and connection, the face recognition module is awakened and controlled The image acquisition module collects the first image of the target object, and thus based on the successful Bluetooth pairing connection and then wakes up the face recognition module, it can effectively reduce the probability of falsely waking up the face recognition module, thereby improving user experience and effectively reducing The power consumption of the face recognition module.

In a possible implementation manner, the search module 31 is used to search for a Bluetooth device with a preset identification via the Bluetooth module provided in the car when the car is in the flameout state or in the flameout state and the door is locked. . In this implementation, there is no need to search for a Bluetooth device with a preset identification through the Bluetooth module before the car is turned off, or there is no need to search for a preset identification through the Bluetooth module before the car is turned off and when the car is turned off but the door is not locked. Bluetooth devices, which can further reduce power consumption.

In a possible implementation, the number of Bluetooth devices with the preset identification is multiple;

The wake-up module 32 is configured to establish a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification in response to searching for any Bluetooth device with the preset identification, or in response to searching for any preset identification The Bluetooth device wakes up and controls the image acquisition module installed in the car to acquire the first image of the target object.

In a possible implementation, the wake-up module 32 includes: a wake-up sub-module for waking up the face recognition module installed in the car; a control sub-module for the wake-up face recognition module The group controls the image acquisition module to acquire the first image of the target object.

In a possible implementation manner, the device further includes: a first control module, configured to control the face recognition module to enter a sleep state if the face image is not collected within a preset time. This implementation method controls the face recognition module to enter a sleep state when no face image is collected within a preset time after the face recognition module is awakened, thereby reducing power consumption.

In a possible implementation manner, the device further includes: a second control module, configured to control the face recognition module to enter a sleep state if the face recognition fails within a preset time. This implementation method controls the face recognition module to enter the sleep state when the face recognition module fails to pass the face recognition within a preset time after waking up the face recognition module, thereby reducing power consumption.

In a possible implementation, the unlocking module 34 is configured to: in response to successful face recognition, determine that the target object has a door opening permission; according to the door of the target object having the door opening permission, send a message to the car At least one of the doors sends a door unlock command and/or a door open command.

In a possible implementation, the face recognition includes: living body detection and face authentication; the face recognition module 33 includes: a face authentication module, which is configured to pass through the image sensor in the image acquisition module The first image is collected, and face authentication is performed based on the first image and pre-registered facial features; the living body detection module is used to collect the corresponding first image through the depth sensor in the image collection module And performing live detection based on the first image and the first depth map.

In a possible implementation, the living body detection module includes: an update sub-module for updating the first depth map based on the first image to obtain a second depth map; and a determining sub-module for obtaining a second depth map based on the The first image and the second depth map determine the live detection result of the target object.

In a possible implementation manner, the image sensor includes an RGB image sensor or an infrared sensor; the depth sensor includes a binocular infrared sensor or a time-of-flight TOF sensor. Using the depth map containing the target object for living body detection can fully mine the depth information of the target object, thereby improving the accuracy of living body detection. For example, when the target object is a human face, the embodiment of the present disclosure uses a depth map containing the human face to perform living body detection, which can fully mine the depth information of the face data, thereby improving the accuracy of living body face detection.

In a possible implementation manner, the TOF sensor adopts a TOF module based on an infrared band. By adopting the TOF module based on the infrared band, the influence of external light on the depth map shooting can be reduced.

In a possible implementation manner, the update submodule is configured to: based on the first image, update the depth value of the depth failure pixel in the first depth map to obtain the second depth map.

In a possible implementation manner, the update sub-module is configured to: determine the depth prediction value and associated information of multiple pixels in the first image based on the first image, wherein The association information indicates the degree of association between the plurality of pixels; based on the depth prediction values and the association information of the plurality of pixels, the first depth map is updated to obtain a second depth map.

In a possible implementation manner, the update submodule is configured to: determine a depth failure pixel in the first depth map; obtain the depth prediction of the depth failure pixel from the depth prediction values of the multiple pixels Value and the depth prediction values of the multiple surrounding pixels of the depth failing pixel; obtaining the degree of association between the depth failing pixel and the plurality of surrounding pixels of the depth failing pixel from the associated information of the plurality of pixels; Based on the depth prediction value of the depth failure pixel, the depth prediction values of a plurality of surrounding pixels of the depth failure pixel, and the degree of association between the depth failure pixel and the surrounding pixels of the depth failure pixel, the determination The updated depth value of the depth failure pixel.

In a possible implementation manner, the update sub-module is configured to: based on the depth prediction value of the surrounding pixels of the depth failure pixel and the relationship between the depth failure pixel and multiple surrounding pixels of the depth failure pixel The degree of association determines the depth associated value of the depth failing pixel; based on the depth prediction value of the depth failing pixel and the depth associated value, determining the updated depth value of the depth failing pixel.

In a possible implementation manner, the update sub-module is configured to: use the degree of association between the depth failure pixel and each surrounding pixel as the weight of each surrounding pixel, The depth prediction values of multiple surrounding pixels are weighted and summed to obtain the depth correlation value of the depth failure pixel.

In a possible implementation manner, the update submodule is configured to determine depth prediction values of multiple pixels in the first image based on the first image and the first depth map.

In a possible implementation manner, the update submodule is used to: input the first image and the first depth map to a depth prediction neural network for processing, and obtain the information of multiple pixels in the first image Depth prediction value.

In a possible implementation manner, the update submodule is configured to: perform fusion processing on the first image and the first depth map to obtain a fusion result; and determine the first image based on the fusion result The depth prediction value of multiple pixels in.

In a possible implementation manner, the update sub-module is configured to: input the first image to a correlation detection neural network for processing, and obtain correlation information of multiple pixels in the first image.

In a possible implementation manner, the update submodule is configured to: obtain an image of the target object from the first image; and update the first depth map based on the image of the target object.

In a possible implementation manner, the update sub-module is used to: obtain key point information of the target object in the first image; based on the key point information of the target object, from the first image Obtain an image of the target object.

In a possible implementation, the update submodule is used to: perform target detection on the first image to obtain the area where the target object is located; perform key point detection on the image of the area where the target object is located to obtain Key point information of the target object in the first image.

In a possible implementation manner, the update submodule is configured to: obtain a depth map of the target object from the first depth map; update the depth map of the target object based on the first image, Obtain the second depth map.

In a possible implementation manner, the determining submodule is configured to: input the first image and the second depth map into a living body detection neural network for processing, and obtain a living body detection result of the target object.

In a possible implementation manner, the determining submodule is configured to: perform feature extraction processing on the first image to obtain first feature information; perform feature extraction processing on the second depth map to obtain a second feature Information; based on the first feature information and the second feature information, determine the live detection result of the target object.

In a possible implementation manner, the determining submodule is configured to: perform fusion processing on the first feature information and the second feature information to obtain third feature information; and determine based on the third feature information The live detection result of the target object.

In a possible implementation manner, the determining submodule is configured to: obtain the probability that the target object is a living body based on the third characteristic information; determine the target object according to the probability that the target object is a living body Live test results.

In a possible implementation, the device further includes an activation and activation module, configured to activate a password unlocking module provided in the car in response to a face recognition failure to initiate a password unlocking process.

In this implementation, password unlocking is an alternative to face recognition unlocking. The reasons for the failure of face recognition may include at least one of the result of the living body detection being that the target object is a prosthesis, the face authentication failure, the failure of image collection (for example, a camera failure), and the number of recognition times exceeding a predetermined number. When the target object does not pass face recognition, the password unlocking process is initiated. For example, the password entered by the user can be obtained through the touch screen on the B-pillar.

In a possible implementation manner, the device further includes a registration module, the registration module is used for one or both of the following: Carrying out car owner registration according to the face image of the car owner collected by the image collection module; The face image of the vehicle owner collected by the terminal device of the vehicle owner is remotely registered, and the registration information is sent to the vehicle, where the registration information includes the face image of the vehicle owner.

Through this implementation, it is possible to perform face comparison based on the pre-registered facial features during subsequent face authentication.

In some embodiments, the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. For specific implementation, refer to the description of the above method embodiments. For brevity, here No longer.

Fig. 14 shows a block diagram of a vehicle face unlocking system according to an embodiment of the present disclosure. As shown in Figure 14, the vehicle face unlocking system includes: a memory 41, a face recognition module 42, an image acquisition module 43 and a Bluetooth module 44; the face recognition module 42 is connected to the memory 41, The image acquisition module 43 is connected to the Bluetooth module 44; the Bluetooth module 44 includes waking up the face recognition when the Bluetooth pairing connection with the Bluetooth device with the preset identification succeeds or the Bluetooth device with the preset identification is searched The microprocessor 441 of the module 42 and the Bluetooth sensor 442 connected to the microprocessor 441; the face recognition module 42 is also provided with a communication interface for connecting with the door domain controller, if the face recognition is successful, Sending control information for unlocking the door to the door domain controller based on the communication interface.

In an example, the memory 41 may include at least one of flash memory (Flash) and DDR3 (Double Date Rate 3, third-generation double data rate) memory.

In an example, the face recognition module 42 may be implemented by SoC (System on Chip).

In an example, the face recognition module 42 is connected to the door domain controller through a CAN (Controller Area Network, Controller Area Network) bus.

In an example, the image acquisition module 43 includes an image sensor and a depth sensor.

In one example, the depth sensor includes at least one of a binocular infrared sensor and a time-of-flight TOF sensor.

In a possible implementation manner, the depth sensor includes a binocular infrared sensor, and two infrared cameras of the binocular infrared sensor are arranged on both sides of the camera of the image sensor. For example, in the example shown in Figure 4a, the image sensor is an RGB sensor, the camera of the image sensor is an RGB camera, and the depth sensor is a binocular infrared sensor. The depth sensor includes two IR (infrared) cameras and two binocular infrared sensors. Two infrared cameras are arranged on both sides of the RGB camera of the image sensor.

In an example, the image acquisition module 43 further includes at least one fill light, the at least one fill light is arranged between the infrared camera of the binocular infrared sensor and the camera of the image sensor, and the at least one fill light includes At least one of the fill light for the image sensor and the fill light for the depth sensor. For example, if the image sensor is an RGB sensor, the fill light used for the image sensor can be a white light; if the image sensor is an infrared sensor, the fill light used for the image sensor can be an infrared light; if the depth sensor is a binocular Infrared sensor, the fill light used for the depth sensor can be an infrared light. In the example shown in FIG. 4a, an infrared lamp is provided between the infrared camera of the binocular infrared sensor and the camera of the image sensor. For example, the infrared lamp can use 940nm infrared.

In a possible implementation manner, the image acquisition module 43 further includes a laser, and the laser is disposed between the camera of the depth sensor and the camera of the image sensor. For example, in the example shown in FIG. 4b, the image sensor is an RGB sensor, the camera of the image sensor is an RGB camera, the depth sensor is a TOF sensor, and the laser is arranged between the camera of the TOF sensor and the camera of the RGB sensor. For example, the laser may be a VCSEL, and the TOF sensor may collect a depth map based on the laser emitted by the VCSEL.

In an example, the depth sensor is connected to the face recognition system 42 through an LVDS (Low-Voltage Differential Signaling) interface.

In a possible implementation, the vehicle face unlocking system further includes: a password unlocking module 45 for unlocking a vehicle door, and the password unlocking module 45 is connected to the face recognition module 42.

In a possible implementation, the password unlocking module 45 includes one or both of a touch screen and a keyboard.

In an example, the touch screen is connected to the face recognition module 42 through FPD-Link (Flat Panel Display Link, flat panel display link).

In a possible implementation, the vehicle face unlocking system further includes a battery module 46, and the battery module 46 is respectively connected to the microprocessor 441 and the face recognition module 42.

In a possible implementation manner, the memory 41, the face recognition module 42, the Bluetooth module 44, and the battery module 46 may be built on an ECU (Electronic Control Unit, electronic control unit).

Fig. 15 shows a schematic diagram of a vehicle face unlocking system according to an embodiment of the present disclosure. In the example shown in Figure 15, the face recognition module is implemented by SoC101, the memory includes flash memory (Flash) 102 and DDR3 memory 103, and the Bluetooth module includes a Bluetooth sensor (Bluetooth) 104 and a microprocessor (MCU, Microcontroller Unit) 105 , SoC101, flash memory 102, DDR3 memory 103, Bluetooth sensor 104, microprocessor 105 and battery module (Power Management) 106 are built on ECU100. The image acquisition module includes depth sensor (3D Camera) 200, and depth sensor 200 passes LVDS The interface is connected with SoC101, the password unlocking module includes a touch screen (Touch Screen) 300, the touch screen 300 is connected with SoC101 through FPD-Link, SoC101 is connected with door domain controller 400 through CAN bus. FIG. 16 shows a schematic diagram of a car according to an embodiment of the present disclosure. As shown in FIG. 16, the vehicle includes a vehicle-mounted face unlocking system 51, and the vehicle-mounted face unlocking system 51 is connected to the door domain controller 52 of the vehicle.

In a possible implementation manner, the image acquisition module is arranged outside the exterior of the vehicle.

In a possible implementation manner, the image acquisition module is arranged in at least one of the following positions: a B-pillar of the vehicle, at least one door, and at least one rearview mirror.

In a possible implementation manner, the face recognition module is arranged in the vehicle, and the face recognition module is connected to the door domain controller via a CAN bus.

The embodiment of the present disclosure also proposes a computer program, including computer readable code, when the computer readable code is executed in an electronic device, the processor in the electronic device is executed to implement the above method.

The embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium.

An embodiment of the present disclosure also provides an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured as the above method.

The electronic device can be provided as a terminal, server or other form of device.

Fig. 17 is a block diagram showing an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.

17, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.

The processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.

The power supply component 806 provides power for various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a microphone (MIC). When the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio signals. The received audio signal may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module. The peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

The sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation. For example, the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components. For example, the component is the display and the keypad of the electronic device 800. The sensor component 814 can also detect the electronic device 800 or the electronic device 800. The position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact. The sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.

In an exemplary embodiment, there is also provided a non-volatile computer-readable storage medium, such as a memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.

The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.

The computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon The protruding structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .

The computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages. Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access the Internet connection). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions. The computer-readable program instructions are executed to realize various aspects of the present disclosure.

Herein, various aspects of the present disclosure are described with reference to flowcharts and/or block diagrams of methods, apparatuses (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and/or block diagrams and combinations of blocks in the flowcharts and/or block diagrams can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowchart and/or block diagram.

It is also possible to load computer-readable program instructions onto a computer, other programmable data processing device, or other equipment, so that a series of operation steps are executed on the computer, other programmable data processing device, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing apparatus, or other equipment realize the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the drawings show the possible implementation architecture, functions, and operations of the system, method, and computer program product according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more functions for implementing the specified logical function. Executable instructions. In some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.

The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Without departing from the scope and spirit of the described embodiments, many modifications and changes are obvious to those of ordinary skill in the art. The choice of terms used herein is intended to best explain the principles, practical applications, or technical improvements of the technologies in the market, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

A method for unlocking a vehicle door is characterized by comprising:

Search for Bluetooth devices with preset identification via the Bluetooth module installed in the car;

In response to searching for the Bluetooth device with the preset identifier, establishing a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identifier;

In response to the successful Bluetooth pairing connection, waking up and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object;

Performing face recognition based on the first image;

In response to successful face recognition, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.
The method according to claim 1, wherein the searching for a Bluetooth device with a preset identifier via the Bluetooth module installed in the car comprises:

When the car is in the off state or in the off state and the door is locked, the Bluetooth module provided in the car searches for a Bluetooth device with a preset identification.
The method according to claim 1 or 2, wherein the number of Bluetooth devices with the preset identification is one.
The method according to claim 1 or 2, wherein the number of Bluetooth devices with the preset identification is multiple;

The establishing a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification in response to searching for the Bluetooth device with the preset identification includes:

In response to searching for any Bluetooth device with a preset identification, a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification is established.
The method according to any one of claims 1 to 4, wherein the awakening and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object comprises:

Waking up the face recognition module installed in the car;

The awakened face recognition module controls the image acquisition module to acquire the first image of the target object.
The method according to claim 5, characterized in that, after waking up the face recognition module installed in the car, the method further comprises:

If the face image is not collected within the preset time, the face recognition module is controlled to enter the sleep state.
The method according to claim 5, characterized in that, after waking up the face recognition module installed in the car, the method further comprises:

If the face recognition is not passed within the preset time, the face recognition module is controlled to enter the sleep state.
The method according to any one of claims 1 to 7, wherein the sending a door unlocking instruction and/or opening a door instruction to at least one door of the vehicle in response to successful face recognition includes:

In response to successful face recognition, determining that the target object has a door opening permission;

Send a door unlocking instruction and/or a door opening instruction to at least one door of the vehicle according to the door of the target object with the door opening permission.
The method according to any one of claims 1 to 8, wherein the face recognition comprises: living body detection and face authentication;

The performing face recognition based on the first image includes:

Acquiring the first image via an image sensor in the image acquisition module, and performing face authentication based on the first image and pre-registered facial features;

A first depth map corresponding to the first image is acquired by a depth sensor in the image acquisition module, and living body detection is performed based on the first image and the first depth map.
The method according to claim 9, wherein the performing live detection based on the first image and the first depth map comprises:

Based on the first image, update the first depth map to obtain a second depth map;

Based on the first image and the second depth map, a live detection result of the target object is determined.
The method according to claim 9 or 10, wherein the image sensor comprises an RGB image sensor or an infrared sensor;

The depth sensor includes a binocular infrared sensor or a time-of-flight TOF sensor.
The method according to claim 11, wherein the TOF sensor adopts a TOF module based on an infrared band.
The method according to any one of claims 10 to 12, wherein the updating the first depth map based on the first image to obtain a second depth map comprises:

Based on the first image, the depth value of the depth failure pixel in the first depth map is updated to obtain the second depth map.
The method according to any one of claims 10 to 13, wherein the updating the first depth map based on the first image to obtain a second depth map comprises:

Based on the first image, determining depth prediction values and associated information of multiple pixels in the first image, wherein the associated information of the multiple pixels indicates the degree of association between the multiple pixels;

Based on the depth prediction values and associated information of the plurality of pixels, the first depth map is updated to obtain a second depth map.
The method according to claim 14, wherein the updating the first depth map based on the depth prediction values and associated information of the multiple pixels to obtain a second depth map comprises:

Determining the depth failure pixels in the first depth map;

Acquiring, from the depth prediction values of the multiple pixels, the depth prediction value of the depth failure pixel and the depth prediction values of multiple surrounding pixels of the depth failure pixel;

Acquiring the degree of association between the depth invalid pixel and the plurality of surrounding pixels of the depth invalid pixel from the associated information of the plurality of pixels;

Based on the depth prediction value of the depth failure pixel, the depth prediction values of a plurality of surrounding pixels of the depth failure pixel, and the degree of association between the depth failure pixel and the surrounding pixels of the depth failure pixel, the determination The updated depth value of the depth failure pixel.
The method according to claim 15, wherein the depth prediction value based on the depth failure pixel, the depth prediction value of a plurality of surrounding pixels of the depth failure pixel, and the depth failure pixel and the The correlation between multiple surrounding pixels of the depth-failed pixel and determining the updated depth value of the depth-failed pixel includes:

Determining the depth correlation value of the depth failure pixel based on the depth prediction value of the surrounding pixels of the depth failure pixel and the degree of association between the depth failure pixel and multiple surrounding pixels of the depth failure pixel;

Determine the updated depth value of the depth failure pixel based on the depth prediction value of the depth failure pixel and the depth correlation value.
The method according to claim 16, wherein the depth prediction value based on the surrounding pixels of the depth invalid pixel and the correlation between the depth invalid pixel and a plurality of surrounding pixels of the depth invalid pixel , Determining the depth associated value of the depth failure pixel includes:

The degree of association between the depth invalid pixel and each surrounding pixel is taken as the weight of each surrounding pixel, and the depth prediction values of multiple surrounding pixels of the depth invalid pixel are weighted and summed to obtain the The depth associated value of the depth failure pixel.
The method according to any one of claims 14 to 17, wherein the determining the depth prediction values of multiple pixels in the first image based on the first image comprises:

Based on the first image and the first depth map, the depth prediction values of a plurality of pixels in the first image are determined.
The method according to claim 18, wherein the determining the depth prediction values of multiple pixels in the first image based on the first image and the first depth map comprises:

The first image and the first depth map are input to a depth prediction neural network for processing to obtain depth prediction values of multiple pixels in the first image.
The method according to claim 18 or 19, wherein the determining the depth prediction values of multiple pixels in the first image based on the first image and the first depth map comprises:

Performing fusion processing on the first image and the first depth map to obtain a fusion result;

Based on the fusion result, the depth prediction values of multiple pixels in the first image are determined.
The method according to any one of claims 14 to 20, wherein the determining the associated information of multiple pixels in the first image based on the first image comprises:

The first image is input to the correlation detection neural network for processing, and correlation information of multiple pixels in the first image is obtained.
The method according to any one of claims 10 to 21, wherein the updating the first depth map based on the first image comprises:

Acquiring an image of the target object from the first image;

Based on the image of the target object, the first depth map is updated.
The method according to claim 22, wherein said acquiring an image of said target object from said first image comprises:

Acquiring key point information of the target object in the first image;

Based on the key point information of the target object, an image of the target object is acquired from the first image.
The method according to claim 23, wherein said acquiring key point information of said target object in said first image comprises:

Performing target detection on the first image to obtain the area where the target object is located;

Perform key point detection on the image of the area where the target object is located to obtain key point information of the target object in the first image.
The method according to any one of claims 10 to 24, wherein said updating said first depth map based on said first image to obtain a second depth map comprises:

Acquiring a depth map of the target object from the first depth map;

Based on the first image, update the depth map of the target object to obtain the second depth map.
The method according to any one of claims 10 to 25, wherein the determining the live detection result of the target object based on the first image and the second depth map comprises:

The first image and the second depth map are input to a living body detection neural network for processing to obtain a living body detection result of the target object.
The method according to any one of claims 10 to 26, wherein the determining the live detection result of the target object based on the first image and the second depth map comprises:

Performing feature extraction processing on the first image to obtain first feature information;

Performing feature extraction processing on the second depth map to obtain second feature information;

Based on the first feature information and the second feature information, a live detection result of the target object is determined.
The method according to claim 27, wherein the determining the live detection result of the target object based on the first characteristic information and the second characteristic information comprises:

Performing fusion processing on the first feature information and the second feature information to obtain third feature information;

Based on the third characteristic information, a living body detection result of the target object is determined.
The method according to claim 28, wherein the determining the live detection result of the target object based on the third characteristic information comprises:

Obtain the probability that the target object is a living body based on the third characteristic information;

Determine the live detection result of the target object according to the probability that the target object is a living body.
The method according to any one of claims 1 to 29, characterized in that, after the face recognition is performed based on the first image, the method further comprises:

In response to the face recognition failure, the password unlocking module provided in the car is activated to start the password unlocking process.
The method according to any one of claims 1 to 30, wherein the method further comprises one or both of the following:

Carrying out vehicle owner registration according to the face image of the vehicle owner collected by the image acquisition module;

Perform remote registration according to the face image of the vehicle owner collected by the terminal device of the vehicle owner, and send registration information to the vehicle, where the registration information includes the face image of the vehicle owner.
A method for unlocking a vehicle door is characterized by comprising:

Search for Bluetooth devices with preset identification via the Bluetooth module installed in the car;

In response to searching for the Bluetooth device with the preset identifier, awakening and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object;

Performing face recognition based on the first image;

In response to successful face recognition, a door unlocking instruction and/or a door opening instruction are sent to at least one door of the vehicle.
The method according to claim 32, wherein the searching for a Bluetooth device with a preset identifier via the Bluetooth module installed in the car comprises:

When the car is in the off state or in the off state and the door is locked, the Bluetooth module provided in the car searches for a Bluetooth device with a preset identification.
The method according to claim 31 or 32, wherein the number of Bluetooth devices with the preset identification is one.
The method according to claim 31 or 32, wherein the number of Bluetooth devices with the preset identification is multiple;

The step of waking up and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object in response to searching for the Bluetooth device with the preset identifier includes:

In response to searching for any Bluetooth device with a preset identifier, wake up and control the image acquisition module provided in the vehicle to acquire the first image of the target object.
The method according to any one of claims 32 to 35, wherein the awakening and controlling the image acquisition module provided in the vehicle to acquire the first image of the target object comprises:

Waking up the face recognition module installed in the car;

The awakened face recognition module controls the image acquisition module to acquire the first image of the target object.
The method according to claim 36, characterized in that, after waking up the face recognition module installed in the car, the method further comprises:

If the face image is not collected within the preset time, the face recognition module is controlled to enter the sleep state.
The method according to claim 36, characterized in that, after waking up the face recognition module installed in the car, the method further comprises:

If the face recognition is not passed within the preset time, the face recognition module is controlled to enter the sleep state.
The method according to any one of claims 32 to 38, wherein in response to successful face recognition, sending a door unlocking instruction and/or a door opening instruction to at least one door of the vehicle comprises:

In response to successful face recognition, determining that the target object has a door opening permission;

Send a door unlocking instruction and/or a door opening instruction to at least one door of the vehicle according to the door of the target object with the door opening permission.
The method according to any one of claims 32 to 39, wherein the face recognition comprises: living body detection and face authentication;

The performing face recognition based on the first image includes:

Acquiring the first image via an image sensor in the image acquisition module, and performing face authentication based on the first image and pre-registered facial features;

A first depth map corresponding to the first image is acquired by a depth sensor in the image acquisition module, and living body detection is performed based on the first image and the first depth map.
The method according to claim 40, wherein said performing live detection based on said first image and said first depth map comprises:

Based on the first image, update the first depth map to obtain a second depth map;

Based on the first image and the second depth map, a live detection result of the target object is determined.
The method according to claim 40 or 41, wherein the image sensor comprises an RGB image sensor or an infrared sensor;

The depth sensor includes a binocular infrared sensor or a time-of-flight TOF sensor.
42. The method according to claim 42, wherein the TOF sensor uses a TOF module based on an infrared band.
The method according to any one of claims 41 to 43, wherein said updating said first depth map based on said first image to obtain a second depth map comprises:

Based on the first image, the depth value of the depth failure pixel in the first depth map is updated to obtain the second depth map.
The method according to any one of claims 41 to 44, wherein said updating said first depth map based on said first image to obtain a second depth map comprises:

Based on the first image, determining depth prediction values and associated information of multiple pixels in the first image, wherein the associated information of the multiple pixels indicates the degree of association between the multiple pixels;

Based on the depth prediction values and associated information of the plurality of pixels, the first depth map is updated to obtain a second depth map.
The method according to claim 45, wherein the updating the first depth map based on the depth prediction values and associated information of the multiple pixels to obtain a second depth map comprises:

Determining the depth failure pixels in the first depth map;

Acquiring, from the depth prediction values of the multiple pixels, the depth prediction value of the depth failure pixel and the depth prediction values of multiple surrounding pixels of the depth failure pixel;

Acquiring the degree of association between the depth invalid pixel and the plurality of surrounding pixels of the depth invalid pixel from the associated information of the plurality of pixels;

Based on the depth prediction value of the depth failure pixel, the depth prediction values of a plurality of surrounding pixels of the depth failure pixel, and the degree of association between the depth failure pixel and the surrounding pixels of the depth failure pixel, the determination The updated depth value of the depth failure pixel.
The method of claim 46, wherein the depth prediction value based on the depth failure pixel, the depth prediction value of a plurality of surrounding pixels of the depth failure pixel, and the depth failure pixel and the The correlation between multiple surrounding pixels of the depth-failed pixel and determining the updated depth value of the depth-failed pixel includes:

Determining the depth correlation value of the depth failure pixel based on the depth prediction value of the surrounding pixels of the depth failure pixel and the degree of association between the depth failure pixel and multiple surrounding pixels of the depth failure pixel;

Determine the updated depth value of the depth failure pixel based on the depth prediction value of the depth failure pixel and the depth correlation value.
The method according to claim 47, wherein the depth prediction value based on the surrounding pixels of the depth failing pixel and the correlation between the depth failing pixel and the plurality of surrounding pixels of the depth failing pixel , Determining the depth associated value of the depth failure pixel includes:

The degree of association between the depth invalid pixel and each surrounding pixel is taken as the weight of each surrounding pixel, and the depth prediction values of multiple surrounding pixels of the depth invalid pixel are weighted and summed to obtain the The depth associated value of the depth failure pixel.
The method according to any one of claims 45 to 48, wherein the determining the depth prediction values of multiple pixels in the first image based on the first image comprises:

Based on the first image and the first depth map, the depth prediction values of a plurality of pixels in the first image are determined.
The method of claim 49, wherein the determining the depth prediction values of multiple pixels in the first image based on the first image and the first depth map comprises:

The first image and the first depth map are input to a depth prediction neural network for processing to obtain depth prediction values of multiple pixels in the first image.
The method according to claim 49 or 50, wherein the determining depth prediction values of multiple pixels in the first image based on the first image and the first depth map comprises:

Performing fusion processing on the first image and the first depth map to obtain a fusion result;

Based on the fusion result, the depth prediction values of multiple pixels in the first image are determined.
The method according to any one of claims 45 to 51, wherein the determining the associated information of multiple pixels in the first image based on the first image comprises:

The first image is input to the correlation detection neural network for processing, and correlation information of multiple pixels in the first image is obtained.
The method according to any one of claims 41 to 52, wherein the updating the first depth map based on the first image comprises:

Acquiring an image of the target object from the first image;

Based on the image of the target object, the first depth map is updated.
The method according to claim 53, wherein said acquiring an image of said target object from said first image comprises:

Acquiring key point information of the target object in the first image;

Based on the key point information of the target object, an image of the target object is acquired from the first image.
The method according to claim 54, wherein the acquiring key point information of the target object in the first image comprises:

Performing target detection on the first image to obtain the area where the target object is located;

Perform key point detection on the image of the area where the target object is located to obtain key point information of the target object in the first image.
The method according to any one of claims 41 to 55, wherein said updating said first depth map based on said first image to obtain a second depth map comprises:

Acquiring a depth map of the target object from the first depth map;

Based on the first image, update the depth map of the target object to obtain the second depth map.
The method according to any one of claims 41 to 56, wherein the determining the live detection result of the target object based on the first image and the second depth map comprises:

The first image and the second depth map are input to a living body detection neural network for processing to obtain a living body detection result of the target object.
The method according to any one of claims 41 to 57, wherein the determining the live detection result of the target object based on the first image and the second depth map comprises:

Performing feature extraction processing on the first image to obtain first feature information;

Performing feature extraction processing on the second depth map to obtain second feature information;

Based on the first feature information and the second feature information, a live detection result of the target object is determined.
The method according to claim 58, wherein the determining the live detection result of the target object based on the first characteristic information and the second characteristic information comprises:

Performing fusion processing on the first feature information and the second feature information to obtain third feature information;

Based on the third characteristic information, a living body detection result of the target object is determined.
The method according to claim 59, wherein the determining the living body detection result of the target object based on the third characteristic information comprises:

Obtain the probability that the target object is a living body based on the third characteristic information;

Determine the live detection result of the target object according to the probability that the target object is a living body.
The method according to any one of claims 32 to 60, characterized in that, after the face recognition is performed based on the first image, the method further comprises:

In response to the face recognition failure, the password unlocking module provided in the car is activated to start the password unlocking process.
The method according to any one of claims 32 to 61, wherein the method further comprises one or both of the following:

Carrying out vehicle owner registration according to the face image of the vehicle owner collected by the image acquisition module;

Perform remote registration according to the face image of the vehicle owner collected by the terminal device of the vehicle owner, and send registration information to the vehicle, where the registration information includes the face image of the vehicle owner.
A vehicle door unlocking device, characterized in that it comprises:

The search module is used to search for the Bluetooth device with the preset identification via the Bluetooth module installed in the car;

The wake-up module is used to establish a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset identification in response to searching for the Bluetooth device with the preset identification, and to wake up and control in response to the successful Bluetooth pairing connection The image acquisition module provided in the vehicle collects the first image of the target object, or, in response to searching for the Bluetooth device with the preset identification, wakes up and controls the image acquisition module provided in the vehicle to acquire the target object First image

A face recognition module, configured to perform face recognition based on the first image;

The unlocking module is used for sending a door unlocking instruction and/or opening a door instruction to at least one door of the vehicle in response to successful face recognition.
The device according to claim 63, wherein the search module is configured to:

When the car is in the off state or in the off state and the door is locked, the Bluetooth module provided in the car searches for a Bluetooth device with a preset identification.
The apparatus according to claim 63 or 64, wherein the number of Bluetooth devices with the preset identification is one.
The device according to claim 63 or 64, wherein the number of Bluetooth devices with the preset identification is multiple;

The wake-up module is used for:

In response to searching for any Bluetooth device with a preset logo, establish a Bluetooth pairing connection between the Bluetooth module and the Bluetooth device with the preset logo, or, in response to searching for any Bluetooth device with a preset logo, wake up and control settings The image acquisition module in the vehicle acquires a first image of the target object.
The device according to any one of claims 63 to 66, wherein the wake-up module comprises:

The wake-up sub-module is used to wake up the face recognition module installed in the car;

The control sub-module is used for controlling the image acquisition module to acquire the first image of the target object by the awakened face recognition module.
The device according to claim 67, wherein the device further comprises:

The first control module is configured to control the face recognition module to enter a sleep state if the face image is not collected within a preset time.
The device according to claim 67, wherein the device further comprises:

The second control module is configured to control the face recognition module to enter a sleep state if the face recognition fails within the preset time.
The method according to any one of claims 63 to 69, wherein the unlocking module is configured to:

In response to successful face recognition, determining that the target object has a door opening permission;

Send a door unlocking instruction and/or a door opening instruction to at least one door of the vehicle according to the door of the target object with the door opening permission.
The device according to any one of claims 63 to 70, wherein the face recognition comprises: living body detection and face authentication;

The face recognition module includes:

The face authentication module is configured to collect the first image via an image sensor in the image acquisition module, and perform face authentication based on the first image and pre-registered facial features;

The living body detection module is configured to collect a first depth map corresponding to the first image via a depth sensor in the image acquisition module, and perform living body detection based on the first image and the first depth map.
The device according to claim 71, wherein the living body detection module comprises:

An update sub-module, configured to update the first depth map based on the first image to obtain a second depth map;

The determining sub-module is configured to determine the living body detection result of the target object based on the first image and the second depth map.
The device according to claim 71 or 72, wherein the image sensor comprises an RGB image sensor or an infrared sensor;

The depth sensor includes a binocular infrared sensor or a time-of-flight TOF sensor.
The device according to claim 73, wherein the TOF sensor adopts a TOF module based on an infrared band.
The device according to any one of claims 72 to 74, wherein the update submodule is configured to:

Based on the first image, the depth value of the depth failure pixel in the first depth map is updated to obtain the second depth map.
The device according to any one of claims 72 to 75, wherein the update submodule is configured to:

Based on the first image, determining depth prediction values and associated information of multiple pixels in the first image, wherein the associated information of the multiple pixels indicates the degree of association between the multiple pixels;

Based on the depth prediction values and associated information of the plurality of pixels, the first depth map is updated to obtain a second depth map.
The device according to claim 76, wherein the update submodule is configured to:

Determining the depth failure pixels in the first depth map;

Acquiring the depth prediction value of the depth failure pixel and the depth prediction values of the multiple surrounding pixels of the depth failure pixel from the depth prediction values of the plurality of pixels;

Acquiring the degree of association between the depth invalid pixel and the plurality of surrounding pixels of the depth invalid pixel from the associated information of the plurality of pixels;

Based on the depth prediction value of the depth failure pixel, the depth prediction values of a plurality of surrounding pixels of the depth failure pixel, and the degree of association between the depth failure pixel and the surrounding pixels of the depth failure pixel, the determination The updated depth value of the depth failure pixel.
The device according to claim 77, wherein the update submodule is configured to:

Determining the depth correlation value of the depth failure pixel based on the depth prediction value of the surrounding pixels of the depth failure pixel and the degree of association between the depth failure pixel and multiple surrounding pixels of the depth failure pixel;

Determine the updated depth value of the depth failure pixel based on the depth prediction value of the depth failure pixel and the depth correlation value.
The device according to claim 78, wherein the update submodule is configured to:

The degree of association between the depth invalid pixel and each surrounding pixel is taken as the weight of each surrounding pixel, and the depth prediction values of multiple surrounding pixels of the depth invalid pixel are weighted and summed to obtain the The depth associated value of the depth failure pixel.
The device according to any one of claims 76 to 79, wherein the update submodule is configured to:

Based on the first image and the first depth map, the depth prediction values of a plurality of pixels in the first image are determined.
The device according to claim 80, wherein the update submodule is configured to:

The first image and the first depth map are input to a depth prediction neural network for processing to obtain depth prediction values of multiple pixels in the first image.
The device according to claim 80 or 81, wherein the update submodule is configured to:

Performing fusion processing on the first image and the first depth map to obtain a fusion result;

Based on the fusion result, the depth prediction values of multiple pixels in the first image are determined.
The device according to any one of claims 76 to 82, wherein the update submodule is configured to:

The first image is input to the correlation detection neural network for processing, and correlation information of multiple pixels in the first image is obtained.
The device according to any one of claims 72 to 83, wherein the update submodule is configured to:

Acquiring an image of the target object from the first image;

Based on the image of the target object, the first depth map is updated.
The device according to claim 84, wherein the update submodule is configured to:

Acquiring key point information of the target object in the first image;

Based on the key point information of the target object, an image of the target object is acquired from the first image.
The device according to claim 85, wherein the update submodule is configured to:

Performing target detection on the first image to obtain the area where the target object is located;

Perform key point detection on the image of the area where the target object is located to obtain key point information of the target object in the first image.
The device according to any one of claims 72 to 86, wherein the update submodule is configured to:

Acquiring a depth map of the target object from the first depth map;

Based on the first image, update the depth map of the target object to obtain the second depth map.
The device according to any one of claims 72 to 87, wherein the determining submodule is configured to:

The first image and the second depth map are input to a living body detection neural network for processing to obtain a living body detection result of the target object.
The device according to any one of claims 72 to 88, wherein the determining submodule is configured to:

Performing feature extraction processing on the first image to obtain first feature information;

Performing feature extraction processing on the second depth map to obtain second feature information;

Based on the first feature information and the second feature information, a live detection result of the target object is determined.
The device according to claim 89, wherein the determining submodule is configured to:

Performing fusion processing on the first feature information and the second feature information to obtain third feature information;

Based on the third characteristic information, a living body detection result of the target object is determined.
The device according to claim 90, wherein the determining submodule is configured to:

Obtain the probability that the target object is a living body based on the third characteristic information;

Determine the live detection result of the target object according to the probability that the target object is a living body.
The device according to any one of claims 63 to 91, wherein the device further comprises:

The activation and activation module is used to activate the password unlocking module provided in the car in response to the face recognition failure to start the password unlocking process.
The device according to any one of claims 63 to 92, wherein the device further comprises a registration module, the registration module being used for one or both of the following:

Carrying out vehicle owner registration according to the face image of the vehicle owner collected by the image acquisition module;

Perform remote registration according to the face image of the vehicle owner collected by the terminal device of the vehicle owner, and send registration information to the vehicle, where the registration information includes the face image of the vehicle owner.
A vehicle-mounted face unlocking system, which is characterized by comprising: a memory, a face recognition module, an image acquisition module, and a Bluetooth module; the face recognition module is connected to the memory, the image acquisition module, and The Bluetooth module is connected; the Bluetooth module includes a microprocessor that wakes up the face recognition module when the Bluetooth pairing connection with a Bluetooth device with a preset identification is successful or when the Bluetooth device with the preset identification is searched for The Bluetooth sensor connected to the microprocessor; the face recognition module is also provided with a communication interface for connecting with the door domain controller, and if the face recognition is successful, the communication interface is used to send the signal to the door domain controller Control information for unlocking the door.
The vehicle-mounted face unlocking system of claim 94, wherein the image acquisition module includes an image sensor and a depth sensor.
The vehicle-mounted face unlocking system according to claim 95, wherein the depth sensor comprises a binocular infrared sensor, and two infrared cameras of the binocular infrared sensor are arranged on both sides of the camera of the image sensor.
The vehicle face unlocking system according to claim 96, wherein the image acquisition module further comprises at least one supplementary light, and the at least one supplementary light is arranged on the infrared camera and the binocular infrared sensor. Between the cameras of the image sensor, the at least one fill light includes at least one of a fill light for the image sensor and a fill light for the depth sensor.
The vehicle-mounted face unlocking system according to claim 95, wherein the image acquisition module further comprises a laser, and the laser is arranged between the camera of the depth sensor and the camera of the image sensor.
The in-vehicle face unlocking system according to any one of claims 94 to 98, wherein the in-vehicle face unlocking system further comprises: a password unlocking module for unlocking a vehicle door, the password unlocking module and the Face recognition module connection.
The in-vehicle face unlocking system according to claim 99, wherein the password unlocking module includes one or both of a touch screen and a keyboard.
The vehicle-mounted face unlocking system according to any one of claims 94 to 100, wherein the vehicle-mounted face unlocking system further comprises: a battery module, and the battery module is connected to the microprocessor and The face recognition module is connected.
A vehicle, characterized in that the vehicle comprises the vehicle-mounted face unlocking system according to any one of claims 94 to 101, and the vehicle-mounted face unlocking system is connected to a door domain controller of the vehicle.
The vehicle of claim 102, wherein the image acquisition module is installed outside the vehicle's exterior.
The vehicle according to claim 103, wherein the image acquisition module is arranged at at least one of the following positions: a B-pillar of the vehicle, at least one door, and at least one rearview mirror.
The car according to any one of claims 102 to 104, wherein the face recognition module is installed in the car, and the face recognition module communicates with the door domain controller via the CAN bus. connection.
An electronic device, characterized in that it comprises:

processor;

A memory for storing processor executable instructions;

Wherein, the processor is configured to execute the method according to any one of claims 1 to 62.
A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the method according to any one of claims 1 to 62 when executed by a processor.
A computer program, comprising computer-readable code, characterized in that, when the computer-readable code runs in an electronic device, a processor in the electronic device executes for implementing any one of claims 1 to 62 The method of the claims.