CN111160202B - Identity verification method, device, equipment and storage medium based on AR equipment - Google Patents
Identity verification method, device, equipment and storage medium based on AR equipment Download PDFInfo
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Abstract
The application provides an identity verification method, device and equipment based on AR equipment and a storage medium, wherein the method comprises the following steps: receiving a live real-time video acquired by preset image acquisition equipment; performing face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked; the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result; and receiving the verification result returned by the server, and displaying the verification result in an information display window. The embodiment of the application can meet the real-time performance of security staff in personnel identity verification.
Description
Technical Field
The present application relates to the field of computer graphics processing technologies, and in particular, to an identity verification method, apparatus, device, and storage medium based on AR devices.
Background
Wisdom garden aims at realizing the interconnection between each plate of traditional garden through internet of things to effectively integrate with the help of technologies such as cloud computing, big data, make the operation of garden infrastructure more intelligent, management more high-efficient, the security is more ensured. At present, the part of the park has realized a certain degree of intellectualization, but the dependence on manpower is still not reduced, and on the park security problem, staff is still required to check the identities of different people in the area, for example: security personnel need to prevent irrelevant suspicious personnel from entering an area which is not allowed to enter, foreground personnel need to confirm and guide information of visitors to a park, and the like. Workers usually check the identity of the access personnel by means of mobile phones or other terminal equipment, but the real-time requirement cannot be met.
Disclosure of Invention
Aiming at the technical problems, the application provides an identity verification method, an identity verification device, identity verification equipment and an identity verification storage medium based on AR equipment, which can meet the real-time performance of security staff in the process of verifying the identity of the staff.
To achieve the above object, a first aspect of an embodiment of the present application provides an identity verification method based on an AR device, including:
receiving a live real-time video acquired by preset image acquisition equipment;
performing face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked;
the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result;
and receiving the verification result returned by the server, and displaying the verification result in an information display window.
With reference to the first aspect, in a possible implementation manner, the performing face detection on the live real-time video through a face detection algorithm includes:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
Filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
and filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame.
With reference to the first aspect, in a possible implementation manner, the tracking the detected face to obtain a face image to be checked includes:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, and determining the face image to be verified from the face images.
With reference to the first aspect, in a possible implementation manner, after the receiving the live real-time video acquired by the preset image acquisition device, the method further includes:
and storing the live real-time video into a preset storage space, and setting the storage duration of the live real-time video.
The second aspect of the embodiment of the application also provides a facial image uploading method, which comprises the following steps:
receiving a face image to be verified sent by AR equipment;
matching the face image to be checked with a standard face image stored in a face database to obtain a checking result;
and returning the verification result to the AR equipment, so that the AR equipment displays the verification result in an information display window.
With reference to the second aspect, in a possible implementation manner, before the matching the face image to be verified with the standard face image stored in the face database to obtain a verification result, the method further includes:
eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
and correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network.
With reference to the second aspect, in one possible implementation manner, the matching the face image to be verified with a standard face image stored in a face database to obtain a verification result includes:
Extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
and taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
With reference to the second aspect, in a possible implementation manner, before the receiving live real-time video acquired by the preset image acquisition device, the method further includes:
collecting standard face images of all users, and extracting the second feature vector of the standard face image of each user;
and correspondingly storing the standard face image of each user, the second feature vector of the standard face image of each user and the identity information of each user into the face database.
A third aspect of the embodiments of the present application provides an identity verification apparatus based on AR equipment, where the apparatus includes:
the video acquisition module is used for receiving the live real-time video acquired by the preset image acquisition equipment;
the face snapshot module is used for carrying out face detection on the live real-time video through a face detection algorithm and tracking the detected face to acquire a face image to be verified;
The image sending module is used for sending the face image to be verified to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result;
and the information display module is used for receiving the verification result returned by the server and displaying the verification result in an information display window.
The fourth aspect of the embodiment of the present application further provides an identity verification apparatus based on AR equipment, where the apparatus includes:
the image receiving module is used for receiving the face image to be verified, which is sent by the AR equipment;
the face comparison module is used for matching the face image to be verified with the standard face image stored in the face database to obtain a verification result;
and the result sending module is used for returning the verification result to the AR equipment so that the AR equipment displays the verification result in an information display window.
A fifth aspect of the embodiments of the present application provides an electronic device, the electronic device including an input device, an output device,
a processor adapted to implement one or more instructions; the method comprises the steps of,
a computer storage medium storing one or more instructions adapted to be loaded by the processor and to perform the AR device-based identity verification method described above.
A sixth aspect of the embodiments of the present application provides a computer storage medium storing one or more instructions adapted to be loaded by a processor and to perform the above-described AR device-based identity verification method.
The scheme of the application at least comprises the following beneficial effects: compared with the prior art, the embodiment of the application receives the live real-time video acquired by the preset image acquisition equipment; then carrying out face detection on the live real-time video through a face detection algorithm, and tracking the detected face to obtain a face image to be checked; the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result; and receiving the verification result returned by the server, and displaying the verification result in an information display window. The AR equipment is adopted to collect video, face detection and face tracking are carried out through an algorithm integrated in the AR equipment, interaction between the AR equipment and the server is achieved based on a fifth generation mobile communication technology, and therefore instantaneity of security staff in personnel identity verification can be met.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a network system architecture diagram provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of an identity verification method based on AR equipment according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a multitasking convolutional neural network according to an embodiment of the present application;
FIG. 4 is an exemplary diagram of an image pyramid provided by an embodiment of the present application;
FIG. 5 is a flowchart of another identity verification method based on AR equipment according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an identity verification device based on AR equipment according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of another identity verification apparatus based on AR equipment according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
The terms "comprising" and "having" and any variations thereof, as used in the description, claims and drawings, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used for distinguishing between different objects and not for describing a particular sequential order.
The network system architecture to which the scheme of the embodiment of the present application may be applied is first described by way of example with reference to the accompanying drawings. Referring to fig. 1, fig. 1 is a diagram of a network system architecture according to an embodiment of the present application, where, as shown in fig. 1, the system architecture at least includes AR glasses, a server, and an information transmission module based on a 5G (5 th generation mobile networks, fifth generation mobile communication technology) network architecture. The AR glasses comprise, but are not limited to, a video acquisition module, a face snapshot module, an information display module and a video storage module, wherein the video acquisition module mainly refers to a camera arranged in the AR glasses and is used for acquiring real-time videos of sites when security staff check identities, the face snapshot module is mainly used for intercepting face images from the videos acquired by the video acquisition module and sending the face images required for subsequent face comparison to a server through the information transmission module, the video storage module is mainly used for storing the videos acquired by the video acquisition module into a memory card arranged in the AR glasses, and the information display module is mainly used for displaying check results of the server on the AR glasses. The server comprises, but is not limited to, an image preprocessing module, an information input module, a face comparison module and a face library module, wherein the image preprocessing module is used for preprocessing face images sent by the AR glasses, such as denoising, enhancing and the like, the information input module is used for inputting standard face images and identity information for workers or users needing registration information, the face comparison module is used for face recognition, the face images sent by the AR glasses are compared with the standard face images in the face library module to obtain verification results, the verification results are returned to the AR glasses through the information transmission module, and the face library module is used for storing the standard face images, the identity information and the face feature vectors of the users, wherein the server can be a remote server. The information transmission module is built on the 5G network, has the advantages of large bandwidth, low delay and low power consumption, and can meet the requirements of high concurrency, low delay and power saving of application. Based on the network system architecture, the interaction time delay of the AR glasses and the server is greatly reduced, the time consumption of identity verification of staff entering a park is effectively shortened, and the real-time performance is guaranteed.
Based on the above description, please refer to fig. 2, fig. 2 is a flow chart of an identity verification method based on AR device according to an embodiment of the present application, where the identity verification method based on AR device is applied to AR device, and the AR device is an executing body, as shown in fig. 2, and includes steps S21-S24:
s21, receiving a live real-time video acquired by preset image acquisition equipment;
in the specific embodiment of the application, the AR equipment is the AR glasses, a chip is arranged in the AR equipment, the image acquisition equipment is a camera arranged in the AR equipment, the camera selects the resolution according to the actual use scene, the resolution in the outdoor scene is usually larger than that in the indoor scene, meanwhile, the resolution of the camera meets the requirement of a server for face comparison, the photometry mode of the camera is set as a central point photometry, and the face of a person to be checked is ensured to be positioned in the central area of a picture as much as possible when the device is used; in order to ensure that the picture does not generate dynamic blur during verification, the shutter parameters of the camera are generally set within 1/200 seconds; the aperture can be dynamically adjusted according to the shutter speed, so long as the exposure of the picture is ensured to be within a reasonable range. When a person enters a specific area, the staff in charge of duty can aim the AR equipment at the face area of the person as much as possible to collect live real-time video.
S22, carrying out face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked;
in a specific embodiment of the present application, the AR device is integrated with a face detection algorithm, for example: target detection algorithm based on AdaBoost framework, deformable component model algorithm, multitasking convolutional neural network algorithm (Multi-task Cascaded Convolutional Networks, MTCNN), yolo target detection algorithm and the like, and in view of the requirement of the scheme on real-time performance and power consumption, a pretrained multitasking convolutional neural network can be adopted for face detection.
In a possible implementation manner, the face detection on the live real-time video through the face detection algorithm includes:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
and filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame.
Specifically, as shown in fig. 3, the structure of the multitasking convolutional neural network includes P-NET, R-NET and O-NET, where the first sub-network is P-NET, the second sub-network is R-NET, and the third sub-network is O-NET, and the output of each sub-network includes classification of whether the face is, bounding box regression and key points of the face. The method comprises the steps of inputting target image frames, namely image frames of a target object, which appear in a live real-time video for the first time, into a P-NET (peer-to-peer) for preliminary face recognition and key point positioning, obtaining a plurality of initial candidate frames, calibrating the initial candidate frames by utilizing bounding box regression, and then performing non-maximum suppression on the initial candidate frames to obtain a certain number of first candidate frames. And taking the output of the P-NET as the input of the R-NET to optimize the detection result of the P-NET, specifically filtering out the non-satisfactory candidate frames in the first candidate frames to obtain a small number of second candidate frames, taking the output of the R-NET as the input of the O-NET to carry out final face recognition and key point detection, continuing to filter the second candidate frames to obtain the central point coordinates, the width and the length of the final face detection frame, and simultaneously displaying the key points of the faces in the target image frames.
In one possible implementation manner, the tracking the detected face to obtain the face image to be checked includes:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, and determining the face image to be verified from the face images.
Specifically, after a face detection algorithm is adopted to obtain a face detection frame, a certain number of points can be selected in the face detection frame to track, preferably, a face centroid, a located face key point and the like can be selected, then target image frames are converted and scaled to construct an image pyramid as shown in fig. 4, then the position of the face centroid in each layer of image is located, the optical flow in each layer of image is calculated, the optical flow of the face centroid in the bottommost layer image (namely the target image frame) of the image pyramid is obtained through layer-by-layer iteration, the corresponding point in the next frame of the face centroid is calculated according to the position of the face centroid in the target image frame and the optical flow, then the corresponding point of the face centroid in the next frame is matched with the face centroid by adopting a Hungary algorithm, the position of the face in the next frame is predicted according to the optimal corresponding point, and tracking is realized. And a plurality of face images are intercepted in the tracking process, then, the definition, the size and the like of each face image are detected, at least one face image with better quality is determined from the plurality of face images, and the face image is used as the face image to be checked.
S23, sending the face image to be verified to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result;
in a specific embodiment of the application, after the AR equipment selects the face image to be checked, the face image to be checked is sent to the server through the information transmission module, after the server receives the face image to be checked, a series of preprocessing operations can be carried out on the face image to be checked, then the face image to be checked is matched with standard face images stored in the face database, a preset number of users with higher matching degree are selected, and identity information of the preset number of users with higher matching degree is used as a checking result.
S24, receiving the verification result returned by the server, and displaying the verification result in an information display window.
In the specific embodiment of the application, the information display window is the display screen of the AR equipment, the server returns the verification result to the AR equipment, the AR equipment analyzes the verification result, then the structured verification result is displayed on the information display window, the real picture and the structured information are overlapped together, and a worker can quickly verify the identity of the target person only by looking at the target person.
It can be seen that the embodiment of the application receives the live real-time video acquired by the preset image acquisition equipment; then carrying out face detection on the live real-time video through a face detection algorithm, and tracking the detected face to obtain a face image to be checked; the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result; and receiving the verification result returned by the server, and displaying the verification result in an information display window. The AR equipment is adopted to collect video, face detection and face tracking are carried out through an algorithm integrated in the AR equipment, interaction between the AR equipment and the server is achieved based on a fifth generation mobile communication technology, and therefore instantaneity of security staff in personnel identity verification can be met.
Referring to fig. 5, fig. 5 is a flowchart of another identity verification method based on AR device according to an embodiment of the present application, where the identity verification method based on AR device is applied to a server, as shown in fig. 5, and includes steps S51-S53:
s51, receiving a face image to be verified, which is sent by the AR equipment;
S52, matching the face image to be verified with the standard face image stored in the face database to obtain a verification result;
and S53, returning the verification result to the AR equipment, so that the AR equipment displays the verification result in an information display window.
In the specific embodiment of the application, the standard face images stored in the face database are face images of registered users, and the registered users have permission to enter and exit the park or a specific area. The face matching may specifically be performed by a geometric feature-based method, a local feature analysis method, a feature face method, a neural network method, etc., which are not limited herein, for example: the feature points can be selected by adopting the feature conversion with unchanged size to match the faces, or the feature extraction can be performed by adopting the convolutional neural network to match the faces, and finally, the identity information of a preset number of users with higher matching degree is used as a verification result and returned to the AR equipment through the information transmission module.
In a possible implementation manner, before the matching the face image to be verified with the standard face image stored in the face database to obtain a verification result, the method further includes:
Eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
and correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network.
In the specific embodiment of the application, in order to improve the accuracy of matching, the face image to be checked needs to be preprocessed before face matching. The Gaussian filter specifically scans each pixel in the face image to be verified through a template, replaces the value of a central pixel point of the template with the weighted average gray value of the pixels in the neighborhood determined by the template, is suitable for eliminating Gaussian noise points, enhances the face image to be verified with the noise points eliminated through a histogram equalization algorithm to improve definition, and finally inputs the face image to be verified after enhancement processing into a trained multitask convolutional neural network to position five face key points of two eyes, a nose and left and right side mouth angles, and corrects the faces based on coordinates of the five face key points. In some examples, a super-resolution algorithm may be added to the preprocessing to further increase the resolution of the face image to be checked.
In a possible implementation manner, the matching the face image to be verified with the standard face image stored in the face database to obtain a verification result includes:
extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
and taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
In the specific embodiment of the application, the convolutional neural network is adopted to perform feature extraction on the face image to be checked after face correction to obtain the multidimensional first feature vector, and meanwhile, in order to improve the matching speed, the scheme can pre-extract and store the second feature vector of the standard face image in the face database, thereby reducing the time consumption of extracting the second feature vector during matching. And (3) calculating the cosine similarity between the first feature vector and the second feature vector by adopting a cosine calculation formula, sequencing according to the sequence from high to low of the cosine similarity, selecting the identity information of the users corresponding to the preset number of standard face images as a verification result, wherein the preset number can be set according to actual conditions.
It can be seen that the embodiment of the application receives the face image to be verified sent by the AR equipment; matching the face image to be checked with a standard face image stored in a face database to obtain a checking result; and returning the verification result to the AR equipment, so that the AR equipment displays the verification result on an information display window, and the real-time performance of security staff in personnel identity verification can be met.
Based on the above description of the method embodiment of fig. 2, please refer to fig. 6, fig. 6 is a schematic structural diagram of an identity verification apparatus based on AR equipment according to an embodiment of the present application, as shown in fig. 6, where the apparatus includes:
the video acquisition module 61 is used for receiving live real-time video acquired by the preset image acquisition equipment;
the face snapshot module 62 is configured to perform face detection on the live real-time video through a face detection algorithm, and track the detected face to obtain a face image to be verified;
an image sending module 63, configured to send the face image to be checked to a server, so that the server matches the face image to be checked with standard face images stored in a face database to obtain a check result;
And the information display module 64 is configured to receive the verification result returned by the server, and display the verification result in an information display window.
In one possible example, the face snapshot module 62 performs face detection on the live real-time video through a face detection algorithm, specifically for:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
and filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame.
In one possible example, the face snapshot module 62 is specifically configured to, in tracking the detected face to obtain the face image to be checked:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
Matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, and determining the face image to be verified from the face images.
Based on the above description of the embodiment of the method of fig. 5, please refer to fig. 7, fig. 7 is a schematic structural diagram of another identity verification apparatus based on AR equipment according to an embodiment of the present application, as shown in fig. 7, the apparatus includes:
an image receiving module 71, configured to receive a face image to be verified sent by the AR device;
a face comparison module 72, configured to match the face image to be verified with a standard face image stored in a face database to obtain a verification result;
and the result sending module 73 is configured to return the verification result to the AR device, so that the AR device displays the verification result in an information display window.
In one possible example, the face comparison module 72 is specifically further configured to, before matching the face image to be verified with the standard face image stored in the face database to obtain a verification result:
eliminating noise points on the face image to be checked by adopting a Gaussian filter;
Performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
and correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network.
In one possible example, the face comparison module 72 is specifically configured to match the face image to be verified with a standard face image stored in a face database to obtain a verification result:
extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
and taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
According to an embodiment of the present application, each unit in the AR device-based identity verification apparatus shown in fig. 6 and 7 may be separately or completely combined into one or several additional units, or some unit(s) thereof may be further split into a plurality of units having smaller functions, which may achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the identity verification apparatus based on the AR device may also include other units, and in practical applications, these functions may also be implemented with assistance of other units, and may be implemented by cooperation of a plurality of units.
According to another embodiment of the present application, the identity verification apparatus device based on an AR device as shown in fig. 6 or fig. 7 may be constructed by running a computer program (including a program code) capable of executing the steps involved in the corresponding method as shown in fig. 2 or fig. 5 on a general-purpose computing device such as a computer including a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), etc., and a storage element, and implementing the face image uploading method of the embodiment of the present application. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and executed by the above-described computing device via the computer-readable recording medium.
Based on the description of the method embodiment and the device embodiment, the embodiment of the application also provides electronic equipment. Referring to fig. 8, the electronic device includes at least a processor 81, an input device 82, an output device 83, and a computer storage medium 84. Wherein the processor 81, input device 82, output device 83, and computer storage medium 84 within the electronic device may be connected by a bus or other means.
The computer storage medium 84 may be stored in a memory of the electronic device, the computer storage medium 84 being for storing a computer program comprising program instructions, the processor 81 being for executing the program instructions stored by the computer storage medium 84. The processor 81, or CPU (Central Processing Unit ), is a computing core as well as a control core of the electronic device, which is adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement a corresponding method flow or a corresponding function.
In one embodiment, the processor 81 of the electronic device provided in the embodiment of the present application may be configured to perform a series of identity verification processes based on the AR device, including:
receiving a live real-time video acquired by preset image acquisition equipment;
performing face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked;
the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result;
and receiving the verification result returned by the server, and displaying the verification result in an information display window.
In one possible example, the processor 81 executing the face detection on the live real-time video by a face detection algorithm includes:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
And filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame.
In one possible example, the processor 81 performs the tracking of the detected face to obtain a face image to be verified, including:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, and determining the face image to be verified from the face images.
In one embodiment, the processor 81 of the electronic device provided in the embodiment of the present application may be further configured to perform another series of identity verification processes based on the AR device, including:
receiving a face image to be verified sent by AR equipment;
matching the face image to be checked with a standard face image stored in a face database to obtain a checking result;
And returning the verification result to the AR equipment, so that the AR equipment displays the verification result in an information display window.
In a possible example, before said matching the face image to be verified with the standard face image stored in the face database to obtain the verification result, the processor 81 is further configured to perform:
eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
and correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network.
In one possible example, the processor 81 performs the matching between the face image to be verified and the standard face image stored in the face database to obtain a verification result, including:
extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
And taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
The electronic device may be an AR device or a server, a cloud server, an edge server, or the like. The electronic devices may include, but are not limited to, a processor 81, an input device 82, an output device 83, and a computer storage medium 84. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of an electronic device and is not limiting of an electronic device, and may include more or fewer components than shown, or certain components may be combined, or different components.
It should be noted that, since the steps in the above-described AR device-based identity verification method are implemented when the processor 81 of the electronic device executes the computer program, the embodiments or implementations of the above-described AR device-based identity verification method are applicable to the electronic device, and the same or similar beneficial effects can be achieved.
The embodiment of the application also provides a computer storage medium (Memory), which is a Memory device in the electronic device and is used for storing programs and data. It will be appreciated that the computer storage medium herein may include both a built-in storage medium in the terminal and an extended storage medium supported by the terminal. The computer storage medium provides a storage space that stores an operating system of the terminal. Also stored in this memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor 81. The computer storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory; alternatively, it may be at least one computer storage medium located remotely from the aforementioned processor 81. In one embodiment, one or more instructions stored in a computer storage medium may be loaded and executed by processor 81 to implement the respective steps described above with respect to the AR device-based identity verification method; in particular implementations, one or more instructions in a computer storage medium are loaded by processor 81 and perform the steps of:
Receiving a live real-time video acquired by preset image acquisition equipment;
performing face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked;
the face image to be verified is sent to a server, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result;
and receiving the verification result returned by the server, and displaying the verification result in an information display window.
In one possible example, one or more instructions in the computer storage medium, when loaded by the processor 81, are further configured to implement the steps of:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
and filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame.
In one possible example, one or more instructions in the computer storage medium, when loaded by the processor 81, are further configured to implement the steps of:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, and determining the face image to be verified from the face images.
In a specific implementation of another embodiment, one or more instructions in a computer storage medium are loaded by processor 81 and perform the steps of:
receiving a face image to be verified sent by AR equipment;
matching the face image to be checked with a standard face image stored in a face database to obtain a checking result;
and returning the verification result to the AR equipment, so that the AR equipment displays the verification result in an information display window.
In one possible example, one or more instructions in the computer storage medium, when loaded by the processor 81, are further configured to implement the steps of:
Eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
and correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network.
In one possible example, one or more instructions in the computer storage medium, when loaded by the processor 81, are further configured to implement the steps of:
extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
and taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
By way of example, the computer storage medium may comprise: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, since the steps in the above-mentioned identity verification method based on AR device are implemented when the computer program of the computer storage medium is executed by the processor, all the embodiments or implementations of the above-mentioned identity verification method based on AR device are applicable to the computer storage medium, and the same or similar beneficial effects can be achieved.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (6)
1. The identity verification method based on the AR equipment is characterized by being applied to the AR equipment in a network system architecture, wherein the AR equipment is equipment worn by security staff during identity verification, the network system architecture further comprises a server and an information transmission module, and the method comprises the following steps:
receiving a live real-time video acquired by preset image acquisition equipment; the image acquisition equipment is a camera of the AR equipment, and the resolution of the camera in an outdoor scene is larger than that of the camera in an indoor scene; the photometry mode of the camera is set as a central point photometry mode, and shutter parameters are set within 1/200 seconds;
Performing face detection on the live real-time video through a face detection algorithm, and tracking the detected face to acquire a face image to be checked; the face detection algorithm is a pretrained multitask convolutional neural network, the input of the multitask convolutional neural network is a target image frame, and the target image frame is an image frame of a target object which appears in the live real-time video for the first time; the tracking of the detected face is realized through a pyramid optical flow algorithm, and tracking points of the pyramid optical flow algorithm comprise the mass center of the face;
the face image to be verified is sent to a server through the information transmission module, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result; the information transmission module is used for transmitting the face image to be verified to a server based on a 5G network;
receiving the verification result returned by the server, and displaying the verification result in an information display window so as to display the verification result and a real picture in a superposition manner;
the step of performing face detection on the live real-time video through a face detection algorithm comprises the following steps:
Inputting the target image frame of the field real-time video into a first sub-network of the multi-task convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame;
the step of tracking the detected face to obtain the face image to be checked includes:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, detecting the definition and the size of the face images, and determining the face image to be checked from the face images.
2. The identity verification method based on the AR equipment is characterized by being applied to a server in a network system architecture, wherein the network system architecture further comprises the AR equipment and an information transmission module, the AR equipment is equipment worn by security staff during identity verification, and the method comprises the following steps:
Receiving a face image to be verified sent by the AR equipment; the face image to be checked is obtained by carrying out face detection on a live real-time video acquired by a preset image acquisition device by the AR device through a face detection algorithm, tracking the detected face and carrying out definition and size detection on a plurality of face images intercepted in the tracking process, wherein the face detection algorithm is a pretrained multitask convolutional neural network, the input of the multitask convolutional neural network is a target image frame, and the target image frame is an image frame of a target object which appears in the live real-time video for the first time; the tracking of the detected face is realized through a pyramid optical flow algorithm, and tracking points of the pyramid optical flow algorithm comprise the mass center of the face; the image acquisition equipment is a camera of the AR equipment, and the resolution of the camera in an outdoor scene is larger than that of the camera in an indoor scene; the photometry mode of the camera is set as a central point photometry mode, and shutter parameters are set within 1/200 seconds;
matching the face image to be checked with a standard face image stored in a face database to obtain a checking result;
Returning the verification result to the AR equipment through the information transmission module, so that the AR equipment displays the verification result in an information display window to display the verification result and a real picture in a superposition mode; the information transmission module returns the verification result to the AR equipment based on a 5G network;
before the face image to be verified is matched with the standard face image stored in the face database to obtain a verification result, the method further comprises the following steps:
eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network;
the step of matching the face image to be checked with the standard face image stored in the face database to obtain a checking result comprises the following steps:
extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
And taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
3. An identity verification device based on an AR device, which is applied to an AR device in a network system architecture, wherein the AR device is a device worn by security staff during identity verification, the network system architecture further includes a server and an information transmission module, and the device includes:
the video acquisition module is used for receiving the live real-time video acquired by the preset image acquisition equipment; the image acquisition equipment is a camera of the AR equipment, and the resolution of the camera in an outdoor scene is larger than that of the camera in an indoor scene; the photometry mode of the camera is set as a central point photometry mode, and shutter parameters are set within 1/200 seconds;
the face snapshot module is used for carrying out face detection on the live real-time video through a face detection algorithm and tracking the detected face to acquire a face image to be verified; the face detection algorithm is a pretrained multitask convolutional neural network, the input of the multitask convolutional neural network is a target image frame, and the target image frame is an image frame of a target object which appears in the live real-time video for the first time; the tracking of the detected face is realized through a pyramid optical flow algorithm, and tracking points of the pyramid optical flow algorithm comprise the mass center of the face;
The image sending module is used for sending the face image to be verified to a server through the information transmission module, so that the server matches the face image to be verified with standard face images stored in a face database to obtain a verification result; the information transmission module is used for transmitting the face image to be verified to a server based on a 5G network;
the information display module is used for receiving the verification result returned by the server and displaying the verification result on an information display window so as to display the verification result and a real picture in a superposition way;
the face snapshot module is used for carrying out face detection on the on-site real-time video through a face detection algorithm, and is particularly used for:
inputting the target image frame of the field real-time video into a first sub-network of a pre-trained multitask convolutional neural network to perform face recognition to obtain a first candidate frame;
filtering the first candidate frame by using a second sub-network of the multitasking convolutional neural network to obtain a second candidate frame;
filtering the second candidate frame and positioning key points of the human face again by using a second sub-network of the multitasking convolutional neural network to obtain a human face detection frame of the human face in the target image frame;
The face snapshot module is specifically used for tracking the detected face to acquire a face image to be checked, and is specifically used for:
taking the center of the face detection frame as a face centroid, and calculating a corresponding point of the face centroid in the next frame of the target image frame by adopting a pyramid optical flow algorithm;
matching the human face centroid with the corresponding point by adopting a Hungary algorithm so as to track the detected human face;
and intercepting a plurality of face images in the process of tracking the detected face, detecting the definition and the size of the face images, and determining the face image to be checked from the face images.
4. An identity verification device based on an AR device, which is characterized in that the device is applied to a server in a network system architecture, the network system architecture further comprises an AR device and an information transmission module, the AR device is a device worn by security staff during identity verification, and the device comprises:
the image receiving module is used for receiving the face image to be verified, which is sent by the AR equipment; the face image to be checked is obtained by carrying out face detection on a live real-time video acquired by a preset image acquisition device by the AR device through a face detection algorithm, tracking the detected face and carrying out definition and size detection on a plurality of face images intercepted in the tracking process, wherein the face detection algorithm is a pretrained multitask convolutional neural network, the input of the multitask convolutional neural network is a target image frame, and the target image frame is an image frame of a target object which appears in the live real-time video for the first time; the tracking of the detected face is realized through a pyramid optical flow algorithm, and tracking points of the pyramid optical flow algorithm comprise the mass center of the face; the image acquisition equipment is a camera of the AR equipment, and the resolution of the camera in an outdoor scene is larger than that of the camera in an indoor scene; the photometry mode of the camera is set as a central point photometry mode, and shutter parameters are set within 1/200 seconds;
The face comparison module is used for matching the face image to be verified with the standard face image stored in the face database to obtain a verification result;
the result sending module is used for returning the verification result to the AR equipment through the information transmission module, so that the AR equipment displays the verification result in an information display window to display the verification result and a real picture in a superposition mode; the information transmission module returns the verification result to the AR equipment based on a 5G network;
the face comparison module is specifically further configured to, before matching the face image to be verified with a standard face image stored in a face database to obtain a verification result:
eliminating noise points on the face image to be checked by adopting a Gaussian filter;
performing enhancement treatment on the de-noised face image to be verified to improve the definition of the face image to be verified;
correcting the face in the face image to be checked with enhanced definition by utilizing a pretrained multitask convolutional neural network;
the face comparison module is specifically used for matching the face image to be verified with the standard face image stored in the face database to obtain a verification result in terms of:
Extracting a first feature vector of the face image to be verified after face correction;
acquiring a second feature vector of the stored standard face image from the face database, and calculating cosine similarity between the first feature vector and the second feature vector;
and taking the identity information of the users corresponding to the first N standard face images as the verification result according to the sequence from the high cosine similarity to the low cosine similarity.
5. An electronic device comprising an input device and an output device, further comprising:
a processor adapted to implement one or more instructions; the method comprises the steps of,
a computer storage medium storing one or more instructions adapted to be loaded by the processor and to perform the AR device-based identity verification method of any one of claims 1-2.
6. A computer storage medium storing one or more instructions adapted to be loaded by a processor and to perform the AR device based identity verification method of any one of claims 1-2.
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| CN112233343A (en) * | 2020-10-19 | 2021-01-15 | 中国工商银行股份有限公司 | Self-service terminal equipment service data processing method and device |
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| CN112818833B (en) * | 2021-01-29 | 2024-04-12 | 中能国际建筑投资集团有限公司 | Face multitasking detection method, system, device and medium based on deep learning |
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