CN115880733A

CN115880733A - Fingerprint unlocking method and device and terminal equipment

Info

Publication number: CN115880733A
Application number: CN202211566993.XA
Authority: CN
Inventors: 何龙钦
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2022-12-07
Filing date: 2022-12-07
Publication date: 2023-03-31

Abstract

The disclosure provides a fingerprint unlocking method and device and terminal equipment, wherein the method is applied to the terminal equipment and comprises the following steps: responding to the detected fingerprint unlocking instruction, and acquiring target environment data of the environment where the terminal equipment is located; determining the target exposure time of the optical sensor when the fingerprint image is acquired according to the target environment data; controlling the optical sensor to continuously expose for the target exposure time so as to acquire the fingerprint image; and unlocking the terminal equipment according to the fingerprint image. The method can dynamically adjust the exposure time according to the environmental data so as to improve the unlocking speed or the unlocking success rate.

Description

Fingerprint unlocking method and device and terminal equipment

Technical Field

The disclosure relates to the technical field of computers, and in particular to a fingerprint unlocking method and device, and a terminal device.

Background

At present, more and more terminal devices are unlocked in a fingerprint unlocking mode. For example, a mobile phone, a door lock, a computer and the like can be unlocked in a fingerprint unlocking mode.

In the process of fingerprint unlocking of the terminal equipment, fingerprint images of users need to be collected for identification, and the quality of the collected fingerprint images in practical application is influenced by environmental factors, so that the success rate of fingerprint unlocking is low, and the fingerprint unlocking speed is low.

Disclosure of Invention

In view of this, the present disclosure provides a fingerprint unlocking method and apparatus, and a terminal device, which can effectively increase a fingerprint unlocking speed while ensuring a success rate of fingerprint unlocking.

According to a first aspect of the embodiments of the present disclosure, a fingerprint unlocking method is provided, where the method is applied to a terminal device, and the method includes:

responding to the detected fingerprint unlocking instruction, and acquiring target environment data of the environment where the terminal equipment is located;

determining the target exposure time of an optical sensor when a fingerprint image is acquired according to the target environment data;

controlling the optical sensor to continuously expose for the target exposure time so as to acquire the fingerprint image;

and unlocking the terminal equipment according to the fingerprint image.

In combination with any one of the embodiments provided by the present disclosure, the determining, according to the target environment data, a target exposure duration of an optical sensor when acquiring a fingerprint image includes:

inputting the target environment data into a pre-trained target neural network, and acquiring a target unlocking result output by the target neural network, wherein the target unlocking result comprises a target fingerprint unlocking success rate;

and determining the exposure time corresponding to the target fingerprint unlocking success rate as the target exposure time according to the corresponding relation between different fingerprint unlocking success rates and different exposure times.

In combination with any one of the embodiments provided by the present disclosure, in the correspondence, the unlocking success rate is negatively correlated with the exposure duration.

In combination with any one of the embodiments provided by the present disclosure, the target neural network is trained in the following manner:

acquiring sample environment data and sample fingerprint unlocking information of a sample fingerprint image in a sample environment;

inputting the sample environment data into an initial neural network to obtain alternative fingerprint unlocking information output by the initial neural network;

determining a loss function based on a difference between the alternative fingerprint unlocking information and the sample fingerprint unlocking information;

and training the initial neural network according to the loss function until the loss function meets a preset training stopping condition to obtain the target neural network.

In combination with any embodiment provided by the present disclosure, the sample fingerprint unlock information includes at least one of:

whether the sample fingerprint unlocking is successful;

an image quality parameter of the sample fingerprint image;

a match metric between the sample fingerprint image and a predetermined standard fingerprint image.

In combination with any embodiment provided by the present disclosure, after determining, according to the target environment data, a target exposure time of the optical sensor when the fingerprint image is acquired, the method further includes:

determining whether the target exposure duration belongs to an abnormal exposure duration;

and when the target exposure duration is determined to belong to the abnormal exposure duration, determining a preset exposure duration as the target exposure duration.

In combination with any one of the embodiments provided by the present disclosure, the determining whether the target exposure duration belongs to an abnormal exposure duration includes:

and judging whether the target exposure time exceeds a preset time threshold.

According to a second aspect of the embodiments of the present disclosure, there is provided a fingerprint unlocking apparatus, the apparatus being applied to a terminal device, the apparatus including:

a data acquisition module to: responding to the detected fingerprint unlocking instruction, and acquiring target environment data of the environment where the terminal equipment is located;

a duration determination module to: determining the target exposure time of the optical sensor when the fingerprint image is acquired according to the target environment data;

a fingerprint acquisition module to: controlling the optical sensor to continuously expose for the target exposure time so as to acquire the fingerprint image;

an identification unlocking module for: and unlocking the terminal equipment according to the fingerprint image.

According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any one of the methods of the first aspect.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a fingerprint unlocking device, including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the steps of the method of any of the first aspect described above when executed by the processor.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

in the scheme, the target exposure time of the optical sensor during fingerprint unlocking can be dynamically adjusted based on analysis of target environment data of the environment where the terminal equipment is located, so that when the environmental factor causes poor fingerprint acquisition effect, the exposure time is prolonged, the quality of a fingerprint image acquired by the optical sensor is higher, the success rate of fingerprint unlocking is increased, and in addition, when the environmental factor causes good fingerprint acquisition effect, the exposure time is reduced so as to accelerate the fingerprint unlocking speed.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a flowchart illustrating a fingerprint unlocking method according to an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a fingerprint unlocking scheme according to an exemplary embodiment of the present disclosure;

FIG. 3 is a block diagram of a fingerprint unlocking device shown in accordance with an exemplary embodiment of the present disclosure;

fig. 4 is a schematic diagram illustrating a structure of a terminal device according to an exemplary embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

The terminology used in the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if," as used herein, may be interpreted as "at \8230; \8230when" or "when 8230; \823030when" or "in response to a determination," depending on the context.

The fingerprint unlocking technology applied to the current market mostly improves the quality of collected fingerprint images by standardizing the method when a user presses a fingerprint, so that the aim of maintaining a high fingerprint unlocking rate is achieved, the fingerprint unlocking speed is improved by optimizing fingerprint template matching strategies and other modes, the influence of environmental factors such as weather and climate on fingerprint collection is not considered, particularly, an optical sensor is poor in adaptability to environmental factors such as temperature and humidity and is extremely easy to be influenced by the environmental factors for collecting the fingerprint, the success rate of fingerprint identification is low, and the fingerprint unlocking speed is low.

Based on this, the embodiment of the disclosure provides a fingerprint unlocking method, which can make full use of surrounding environment information during fingerprint unlocking to adjust exposure time of an optical sensor for collecting a fingerprint image, thereby optimizing fingerprint unlocking performance and improving user experience.

As shown in fig. 1, fig. 1 is a flowchart of a fingerprint unlocking method, which may be used for a terminal device to be unlocked by a fingerprint, according to at least one embodiment of the present disclosure, and includes the following steps:

in step 102, in response to detecting a fingerprint unlocking instruction, target environment data of the environment where the terminal device is located is acquired.

Terminal equipment refers to the electronic equipment who has fingerprint unblock function, has the optics fingerprint module of gathering the fingerprint image, for example, can be cell-phone, notebook computer, panel computer, intelligent lock etc..

The embodiment does not limit the way of detecting the fingerprint unlocking instruction, for example, the fingerprint unlocking instruction may be triggered by touching a touch screen of the terminal device with a finger, the terminal device detects the fingerprint unlocking instruction when sensing the touch of the finger, or triggered by a button on the terminal device, and the fingerprint unlocking instruction is detected when the terminal device detects that the button is pressed.

The target environment data of terminal equipment place environment is the data relevant with the humiture, and the humiture can influence the quality that fingerprint image was gathered to the optics fingerprint module, for example, the environmental data includes one of following at least: the local air temperature; ambient humidity; longitude and latitude; indoor temperature; indoor humidity; and the nuclear temperature of the terminal equipment.

The environment data can be acquired by the sensor of the terminal device, and can also be acquired by the sensors of other devices. For example, for a mobile phone, a built-in temperature sensor of the mobile phone can acquire the core temperature of the mobile phone, and a GPS module of the mobile phone can acquire longitude and latitude; for another example, a plurality of hygrothermographs are installed near the terminal device, the hygrothermographs can synchronize the collected temperature and humidity to the terminal device, and when the terminal device is located indoors and the hygrothermographs exist indoors, the data of the hygrothermograph closest to the terminal device can be obtained; for another example, a temperature sensor and a humidity sensor may be built in the terminal device to acquire the temperature and humidity of the environment.

In step 104, a target exposure duration of the optical sensor when the fingerprint image is acquired is determined according to the target environment data.

The optical sensor refers to a photosensitive element for collecting a fingerprint image in an optical fingerprint module, and is used for collecting a fingerprint by an optical fingerprint collection technology, for example, the optical sensor may be a CCD (Charge-coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor) sensor, and can generate a corresponding Charge signal according to light irradiated on a surface thereof.

The optical sensor is generally disposed below the touch screen, for example, in a mobile phone, when a finger is pressed on a screen of the mobile phone, a screen area below the finger emits strong light to illuminate the surface of the whole finger, the light is reflected by the finger and enters the screen again, the light is imaged on a CCD or CMOS sensor below the screen through a lens below the screen, and the photosensitive elements acquire a fingerprint image and then compare the fingerprint image with an entered fingerprint in a memory to achieve an authentication effect.

After light source or screen luminescence in the optical fingerprint module is promptly to exposure time, optical sensor's sensitization is long, and exposure time is longer, and the fingerprint detail in the fingerprint image of gathering is abundant more, and image quality is just higher, and the success rate when fingerprint image is used for the unblock is also higher.

For the optics fingerprint module, there is certain requirement to operational environment's humiture, temperature or humidity are too high or low, all can influence its quality of gathering the fingerprint image in fixed exposure duration, so, when temperature or humidity are unsuitable to lead to the fingerprint to gather the effect not good, can promote image quality through extension exposure time to promote fingerprint unblock success rate; and when the suitable optics fingerprint module during operation of humiture, the fingerprint image of gathering in the short time unlocks with enough, can accelerate fingerprint unblock speed through reducing exposure time.

In this step, the target exposure duration of the optical sensor when the fingerprint image is collected is determined according to the target environment data, which may be a mapping relationship between the environmental data and the exposure duration set in advance, so as to determine the target exposure duration according to the target environment data.

In one embodiment, in order to fully utilize the target environment data and improve the effect of dynamically adjusting the target exposure duration, this step may use a deep learning neural network technique, and the determining the target exposure duration of the optical sensor when acquiring the fingerprint image according to the target environment data includes:

The target neural network may be a convolutional neural network, such as Fast RCNN (convolutional neural network based on acceleration region), fast RCNN (convolutional neural network based on Fast region), and R-CNN (convolutional neural network based on region), and the embodiment does not limit the specific neural network and the training method used in the target neural network.

The target unlocking result is a current unlocking result predicted by the target neural network according to the target environment data, and comprises a target fingerprint unlocking success rate, for example, the confidence that the current fingerprint unlocking result output by the target neural network belongs to successful unlocking is positive correlation with the target fingerprint unlocking success rate, and the higher the confidence is, the higher the target fingerprint unlocking success rate is; the lower the confidence, the lower the target fingerprint unlocking success rate.

The target unlocking result can also comprise a predicted image quality parameter of the fingerprint image acquired this time and a predicted matching degree parameter between the fingerprint image acquired this time and a predetermined standard fingerprint image. The meaning represented by the parameters in the target unlocking result is the same, and the higher the target fingerprint unlocking success rate is, the higher the possibility that the fingerprint image acquired at this time is a high-quality image is, and the higher the matching degree parameter between the fingerprint image acquired at this time and the predetermined standard fingerprint image is.

According to different unlocking success rates of the fingerprints, the exposure duration can be adjusted, the corresponding relationship between the two can be set by a person skilled in the art according to actual needs, and the embodiment does not limit the exposure duration. For example, the binary correspondence may be a functional relationship or a one-to-one correspondence, so that the unique target exposure duration can be determined according to the target fingerprint unlocking success rate.

In one embodiment, in the correspondence, the unlocking success rate is negatively correlated with the exposure duration. For example, when the success rate of fingerprint unlocking is low, the image quality can be improved by prolonging the exposure time, so that the success rate of fingerprint unlocking is improved; when the success rate of fingerprint unlocking is high, the fingerprint image acquired in a short time can be sufficiently unlocked, and the fingerprint unlocking speed can be accelerated by reducing the exposure time. For another example, the exposure time of three gears A, B and C can be divided, wherein the exposure gear A has the longest time and the exposure gear B has the shortest time, and if the success rate of fingerprint unlocking in the scene is poor and is lower than a preset lowest threshold, the exposure gear A is adopted to increase the exposure time so as to ensure that the fingerprint sampling can obtain enough characteristics; if the fingerprint unlocking success rate is higher than a preset highest threshold value in the scene, adopting an exposure gear B to reduce the exposure time so as to increase the unlocking speed as much as possible; of course, if the fingerprint unlocking success rate in this scenario is at a general level, not higher than the highest threshold and not lower than the lowest threshold, the exposure gear C, that is, the original fixed standard exposure duration, is adopted.

In one embodiment, the target neural network is trained in the following manner:

A large amount of training sample data is collected in advance: in a certain scene, obtaining weather information such as local temperature, environment humidity, longitude and latitude and the like, sensor data such as current nuclear temperature of the terminal equipment and the like before unlocking by using the terminal equipment each time, and obtaining data of a temperature and humidity meter closest to the terminal equipment under the condition that the indoor temperature and humidity meter exists; after unlocking is completed, the sample environment data and sample fingerprint unlocking information of the current fingerprint unlocking, such as the result of whether the fingerprint unlocking is successful or not, the image acquisition quality, the matching score and the like, are stored locally. Therefore, according to different scenes, after enough data are collected in a plurality of sample environments, the training of the convolutional neural network can be prepared, and the initial neural network is adjusted to a model with the environmental data corresponding to the unlocking result through the training.

According to the collected training sample data, setting related training parameters, and performing convolutional neural network training, which may specifically be: inputting sample environment data into an initial neural network, performing feature extraction on the sample environment data by using the initial neural network, predicting to obtain alternative fingerprint unlocking information based on the extracted features, wherein the alternative fingerprint unlocking information can comprise confidence of successful unlocking of the sample fingerprint or confidence of failed unlocking of the sample fingerprint, the confidence ranges from 0 to 1, the actual sample fingerprint unlocking information is successful unlocking or failed unlocking and can be respectively represented by 0 and 1, and network loss can be calculated through a loss function based on the difference between the confidence and the actual sample fingerprint unlocking information.

The loss function is used to determine the difference between the actual output and the expected output, and the embodiment is not limited to which loss function is specifically used. For example, a quantile loss function, a mean square error loss function, or a cross entropy loss function may be used.

In particular implementations, network parameters in the initial neural network can be adjusted by back propagation according to a loss function. And when a preset training stopping condition is reached, ending the network training, wherein the training stopping condition can be that iteration reaches a certain number of times or the network loss is less than a certain threshold value. A large amount of data is required for training, and a relatively reliable target neural network is obtained.

In the training process, weather information such as local temperature, environmental humidity, longitude and latitude before unlocking, sensor data such as the current nuclear temperature of the mobile phone and the like are collected, data of the closest hygrothermograph is also collected as input sample environmental data under the condition that an indoor hygrothermograph exists, unlocking result data under the scene data are collected as a label, and a convolutional neural network model corresponding to the environmental data and the unlocking result is established.

In one embodiment, the sample fingerprint unlock information includes at least one of:

whether the sample fingerprint unlocking is successful;

an image quality parameter of the sample fingerprint image;

Whether the sample fingerprint unlocking is successful or not can be the sample fingerprint unlocking success or the sample fingerprint unlocking failure; the image quality parameter of the sample fingerprint image is used for evaluating the quality of the sample fingerprint image collected by an optical fingerprint module in the terminal equipment, and can be related to the definition and integrity of the fingerprint image; the matching degree parameter between the sample fingerprint image and the predetermined standard fingerprint image is used for representing the similarity of the sample fingerprint image and the standard fingerprint image of the same user which is pre-recorded.

It should be noted that, when the sample fingerprint unlocking information includes more than two options, the multiple options serve as multiple label categories to label corresponding sample environment data, and the candidate fingerprint unlocking information includes multiple corresponding output values. For example, when the sample fingerprint unlocking information includes whether the sample fingerprint unlocking is successful and the image quality parameter of the sample fingerprint image, the alternative fingerprint unlocking information includes the sample fingerprint unlocking success rate and the predicted value of the image quality parameter of the sample fingerprint image, and when the network loss is determined, the loss function can be calculated according to the difference between the sample fingerprint unlocking success rate and the difference between the image quality parameter of the sample fingerprint image and the predicted value of the image quality parameter, so as to obtain the network loss. The training of the target neural network is carried out by using the multi-label labeled training sample, so that the robustness and the accuracy of the target neural network can be improved.

In step 106, the optical sensor is controlled to be continuously exposed for the target exposure duration to acquire the fingerprint image.

And controlling the optical sensor in the terminal device to continuously expose within the target exposure duration, wherein at the moment, the light source in the terminal device also needs to start the target exposure duration so that the optical sensor continuously senses the imaging of the light emitted by the light source after being reflected by the finger within the target exposure duration, and further collects the fingerprint image of the user.

The longer the exposure time, the more fingerprint characteristics that the optical sensor can acquire, the richer the fingerprint details that are acquired, and the better the quality of the fingerprint image.

In step 108, the terminal device is unlocked according to the fingerprint image.

And identifying and analyzing the fingerprint image, and unlocking the terminal equipment when the fingerprint image is determined to be consistent with a standard fingerprint image stored in a fingerprint database in advance, for example, the similarity reaches a preset similarity threshold, wherein the unlocking can be performed on a screen, a door lock and the like.

In an implementation manner, on the basis of the above embodiment, after the determining, according to the target environment data, the target exposure time period of the optical sensor when the fingerprint image is acquired, the method further includes:

In this embodiment, an abnormal value edge is additionally determined for the determined target exposure duration, whether the target exposure duration belongs to the abnormal exposure duration is determined, and if the target exposure duration belongs to the abnormal exposure duration, the preset exposure duration is used as a new target exposure duration, and the preset exposure duration is a native standard exposure duration, so as to avoid that the abnormal value affects user experience.

In one example, the determining whether the target exposure time length belongs to an abnormal exposure time length includes:

and judging whether the target exposure time exceeds a preset time threshold.

The preset duration threshold may include a highest duration threshold of an upper limit, or a lowest duration threshold of a lower limit, and may further include the two thresholds, and when the target exposure duration exceeds the lower limit or the upper limit, it is determined that the target exposure duration belongs to the abnormal exposure duration.

For example, when a sensor for measuring environmental data fails to work, a certain item of environmental data is lost or abnormal, and further the exposure duration of the target determined based on the target environmental data is far higher or far lower than a general value, or the exposure durations of the target used in multiple unlocking are all a certain value and are not dynamically adjusted along with the change of environmental factors, or the user does not unlock successfully in the given exposure duration of the target once or multiple times, the present embodiment introduces abnormal value edge judgment to avoid the occurrence of the above abnormal situation.

For example, when it is determined that the target exposure duration does not belong to the abnormal exposure duration, the retry0 is unlocked (the same user attempts unlocking for the first time), the target exposure duration determined based on the target environment data is adopted, if the target exposure duration is not successfully unlocked, the retry1 is unlocked next time, the target exposure duration can be determined to belong to the abnormal exposure duration, and the preset exposure duration is determined to be the new target exposure duration.

While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently.

Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.

Exemplarily, taking a terminal device as a mobile phone as an example, a complete fingerprint unlocking scheme of the present disclosure is shown in fig. 2.

The method comprises the steps of firstly, in a data acquisition stage, unlocking in a large number of different unlocking scenes, acquiring weather information such as local temperature, environment humidity, longitude and latitude before unlocking and sensor data such as core temperature of a mobile phone at the time when unlocking is performed each time, acquiring a data set used for training according to unlocking result data, and establishing an unlocking scene recognition library to store different unlocking scenes. And carry out many labels classification labels to the data set, for example, to an environmental data that has contained humiture, label its label as according to the unblock result: the unlocking is successful, the image acquisition quality is high, and the matching degree is 98%.

And then, in a training stage, training the deep convolutional neural network according to the training set labeled by the multi-label category, and training to obtain a deep convolutional neural network model corresponding to the environmental data and the unlocking result.

Finally, in the application stage, real-time environment data is collected before the on-site user actually unlocks, the real-time environment data is input into the trained deep convolutional neural network model, the unlocking result is obtained through prediction, and the corresponding exposure time is output according to the unlocking result and is used as the exposure time scheme of the unlocking; further judging whether the exposure time length belongs to a credible space, if so, judging that the exposure time length is not abnormal exposure time length, outputting a final exposure time length scheme of the unlocking, and controlling the optical sensor to expose according to the exposure time length when acquiring the fingerprint; if not, the exposure duration is the abnormal exposure duration, the exposure duration scheme is abandoned, and the optical sensor is controlled to be exposed according to the original exposure duration when the fingerprint is collected.

Corresponding to the embodiment of the application function implementation method, the disclosure also provides an embodiment of an application function implementation device and a corresponding terminal.

Referring to fig. 3, a block diagram of a fingerprint unlocking apparatus according to an exemplary embodiment, the apparatus is applied to a terminal device, and the apparatus may include:

a data acquisition module 31 configured to: responding to the detected fingerprint unlocking instruction, and acquiring target environment data of the environment where the terminal equipment is located;

a duration determination module 32 configured to: determining the target exposure time of the optical sensor when the fingerprint image is acquired according to the target environment data;

a fingerprint acquisition module 33 configured to: controlling the optical sensor to continuously expose for the target exposure time so as to acquire the fingerprint image;

an identification unlocking module 34 for: and unlocking the terminal equipment according to the fingerprint image.

In some embodiments, the duration determining module 32 is specifically configured to:

In some embodiments, in the correspondence, the unlocking success rate is inversely related to the exposure time.

In some embodiments, the target neural network is trained in the following manner:

In some embodiments, the sample fingerprint unlock information includes at least one of:

whether the sample fingerprint unlocking is successful;

an image quality parameter of the sample fingerprint image;

In some embodiments, after determining the target exposure time of the optical sensor when acquiring the fingerprint image according to the target environment data, the time length determination module 32 is further configured to:

In some embodiments, the duration determining module 32, when configured to determine whether the target exposure duration belongs to the abnormal exposure duration, is specifically configured to: and judging whether the target exposure time exceeds a preset time threshold.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement without inventive effort.

Accordingly, in one aspect, an embodiment of the present disclosure provides a terminal device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute executable instructions to implement the fingerprint unlocking method of any one of the above.

Referring to fig. 4, the terminal device 400 may include one or more of the following components: processing components 402, memory 404, power components 406, multimedia components 408, audio components 410, input/output (I/O) interfaces 412, sensor components 414, and communication components 416.

The processing component 402 generally controls overall operation of the terminal device 400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 402 can include one or more modules that facilitate interaction between the processing component 402 and other components. For example, the processing component 402 can include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

The memory 404 is configured to store various types of data to support operations at the device 400. Examples of such data include instructions for any application or method operating on the terminal device 400, contact data, phonebook data, messages, pictures, videos, and the like. The memory 404 may be implemented by any type or combination of volatile or non-volatile memory devices, 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 or optical disks.

The power supply component 406 provides power to the various components of the terminal device 400. The power components 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device 400.

The multimedia component 408 includes a screen providing an output interface between the terminal device 400 and the user as described above. 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or slide action but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 400 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 410 is configured to output and/or input audio signals. For example, the audio component 410 includes a Microphone (MIC) configured to receive an external audio signal when the terminal apparatus 400 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 404 or transmitted via the communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

The I/O interface 412 provides an interface between the processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor component 414 includes one or more sensors for providing various aspects of status assessment for the terminal device 400. For example, sensor assembly 414 can detect the open/closed status of device 400, the relative positioning of components, such as a display and keypad of terminal device 400, sensor assembly 414 can also detect a change in the position of terminal device 400 or a component of terminal device 400, the presence or absence of user contact with terminal device 400, orientation or acceleration/deceleration of terminal device 400, and a change in the temperature of terminal device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 416 is configured to facilitate wired or wireless communication between the terminal device 400 and other devices. The terminal device 400 may access a wireless network based on a communication standard, such as WiFi,2G or 3g,4g LTE, 5G NR, or a combination thereof. In an exemplary embodiment, the communication component 416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may 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 terminal device 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 404, including instructions that, when executed by the processor 420 of the terminal device 400, enable the terminal device 400 to perform any of the fingerprint unlocking methods described above, is also provided.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fingerprint unlocking method is applied to a terminal device and comprises the following steps:

determining the target exposure time of the optical sensor when the fingerprint image is acquired according to the target environment data;

and unlocking the terminal equipment according to the fingerprint image.

2. The method of claim 1, wherein determining a target exposure time of an optical sensor when acquiring the fingerprint image according to the target environment data comprises:

3. The method according to claim 2, wherein in the correspondence, the unlocking success rate is inversely related to the exposure duration.

4. The method of claim 2, wherein the target neural network is trained by:

5. The method of claim 4, wherein the sample fingerprint unlock information comprises at least one of:

whether the sample fingerprint unlocking is successful;

an image quality parameter of the sample fingerprint image;

6. The method of claim 1, wherein after determining a target exposure time period of an optical sensor at the time of acquiring the fingerprint image based on the target environment data, the method further comprises:

7. The method of claim 6, wherein the determining whether the target exposure time duration belongs to an abnormal exposure time duration comprises:

and judging whether the target exposure time exceeds a preset time threshold.

8. A fingerprint unlocking device is characterized in that the device is applied to a terminal device, and the device comprises:

9. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the fingerprint unlocking method according to any one of claims 1 to 7.

10. A terminal device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to execute the executable instructions to implement the steps of the fingerprint unlocking method according to any one of claims 1 to 7.