WO2020038136A1 - Procédé et appareil de reconnaissance faciale, dispositif électronique et support lisible par ordinateur - Google Patents

Procédé et appareil de reconnaissance faciale, dispositif électronique et support lisible par ordinateur Download PDF

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WO2020038136A1
WO2020038136A1 PCT/CN2019/095286 CN2019095286W WO2020038136A1 WO 2020038136 A1 WO2020038136 A1 WO 2020038136A1 CN 2019095286 W CN2019095286 W CN 2019095286W WO 2020038136 A1 WO2020038136 A1 WO 2020038136A1
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similarity
pictures
similarities
database
facial
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PCT/CN2019/095286
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Chinese (zh)
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张站朝
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深圳前海达闼云端智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

Definitions

  • the present application relates to the technical field of computer information processing, and in particular, to a facial recognition method, device, electronic device, and computer-readable medium.
  • Face recognition technology is a biometric recognition technology based on human face feature information for identification in recent years. It is also currently the most widely used artificial intelligence technology based on deep learning. Compared with other biometric technologies, face recognition has many advantages such as friendliness, simplicity, accuracy, economy, and good scalability. It can be widely used in many aspects such as security verification, monitoring, and access control. At present, face recognition technology has been Apply to access control attendance, visitor management, patrol and other places.
  • face recognition has at least the following problems: When performing face recognition in natural scenes, when the preset face feature database contains a large number of face features (such as millions of faces) Library), affected by the lighting, resolution, blurring, angle and other factors of the collected face image. Due to the large sample space of the face feature comparison, the possibility of high similarity of face features increases, so, The problem of reduced recognition accuracy will occur in the actual face recognition process
  • the purpose of the embodiments of the present application is to provide a facial recognition method, device, electronic device, and computer-readable medium, which can quickly and accurately perform facial feature recognition on a human face in the case of massive data, and output a recognition result.
  • a face recognition method includes: comparing a face image to be recognized with a plurality of first pictures in a database, and obtaining a plurality of first similarities; When the largest first similarity in the first similarity is within the first similarity threshold, a part of the first similarities in the plurality of first similarities are extracted; a plurality of first similarities corresponding to the partial first similarities are determined to be multiple Two second pictures; comparing a second facial image to be recognized with a plurality of second pictures to obtain a plurality of second similarities; and determining the facial image to be recognized according to the plurality of second similarities Facial feature recognition results.
  • a face recognition device includes a first comparison module configured to perform a first similarity comparison between a face image to be recognized and a plurality of first pictures in a database, and obtain a plurality of first A similarity; a threshold module, configured to extract a portion of the first similarity among the multiple first similarities when the largest first similarity among the multiple first similarities is within the first similarity threshold; the second picture A generation module for determining a plurality of second pictures from a plurality of first pictures corresponding to a portion of the first similarity; a second comparison module for comparing a second face image to be recognized with a plurality of second pictures To obtain a plurality of second similarities; and a first result module configured to determine a facial feature recognition result of the facial image to be recognized according to the plurality of second similarities.
  • an electronic device includes: one or more processors; a storage device for storing one or more programs; and when one or more programs are processed by one or more processors Execute so that one or more processors implement the method as above.
  • a computer-readable medium is provided on which a computer program is stored, and the program implements the method as described above when executed by a processor.
  • the face recognition method, device, electronic device, and computer-readable medium of the present disclosure it is possible to quickly and accurately perform facial feature recognition on a human face in the case of massive data, and output a recognition result.
  • An embodiment of the present application further provides a computer program product.
  • the computer program product includes a computing program stored on a non-volatile computer-readable storage medium.
  • the computer program includes program instructions. When the program instructions are executed by the computer, the computer causes the computer to Perform the method described above.
  • FIG. 1 is a flowchart of a facial recognition method according to a first embodiment of the present application
  • FIG. 2 is a flowchart of a facial recognition method according to a second embodiment of the present application.
  • FIG. 3 is a flowchart of a facial recognition method according to a third embodiment of the present application.
  • FIG. 4 is a flowchart of a facial recognition method according to a fourth embodiment of the present application.
  • FIG. 5 is a flowchart of a facial recognition method according to a fifth embodiment of the present application.
  • FIG. 6 is a functional block diagram of a facial recognition device according to a sixth embodiment of the present application.
  • FIG. 7 is a functional block diagram of a facial recognition device according to a seventh embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of an electronic device according to an eighth embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a computer-readable storage medium according to a ninth embodiment of the present application.
  • first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Accordingly, the first component discussed below may be referred to as the second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and / or” includes any combination of any one and one or more of the associated listed items.
  • the area feature analysis algorithm widely used in face recognition technology uses deep learning technology to extract portrait feature points from videos and photos, and uses the principle of biostatistics to analyze and establish mathematical models, that is, faces Feature template.
  • the best matching facial feature template is searched, and a person's Identity Information.
  • the extracted facial feature data is searched and matched with the feature templates stored in the database, and an optimal similarity threshold is set. When the similarity exceeds the threshold, the matched result is output.
  • this application proposes a face recognition method.
  • the attribute recognition of age and gender can be added, which can significantly improve the accuracy of face recognition in a massive data search scenario.
  • the facial recognition method proposed in this application can be set on a remote server (such as a cloud server) or in a detection device (facial recognition device), which is not limited in this application.
  • FIG. 1 is a flowchart of a facial recognition method according to a first embodiment of the present application.
  • the facial recognition method 10 includes at least steps S102 to S110.
  • a first similarity comparison is performed between a face image to be identified and a plurality of first pictures in a database to obtain a plurality of first similarities.
  • the first similarity comparison is a comparison of facial features.
  • feature points of a facial image to be identified may be extracted; the feature points may be compared with feature points of a plurality of first pictures in a database; and the plurality of first image points may be obtained according to a comparison result. A similarity.
  • face model training and face feature extraction are processes that convert a face image into a series of fixed-length values.
  • This numerical string is called “Face Feature” and has the ability to characterize this face.
  • the input of the facial feature extraction process is also "a face image” and “coordinates of the key points of the facial features”, and the output is a numerical string (feature) corresponding to the face.
  • the model training module uses deep neural networks to train feature extraction models based on big face data. This model is used for subsequent face recognition comparisons.
  • Face recognition comparison includes three processes: face image preprocessing, feature extraction, and feature comparison.
  • Face preprocessing includes two processes: face detection and face registration.
  • Face comparison is an algorithm that measures the similarity between two faces. The input of the face comparison algorithm is two face features (the face features are obtained by the previous face feature extraction algorithm), and the output is the similarity between the two features.
  • a part of the first similarities among the plurality of first similarities is extracted.
  • Within the first similarity threshold may be, for example, 55% -65%.
  • all the first similarities among the plurality of first similarities that are within the first similarity threshold may be extracted.
  • a plurality of second pictures are determined from a plurality of first pictures corresponding to a portion of the first similarity.
  • determining the user IDs corresponding to a plurality of first pictures corresponding to the first similarity in the database merging the first pictures having the same user ID; and generating a plurality of first pictures according to the merged result.
  • Two pictures and corresponding first similarities are weighted and combined.
  • TOP1 when the highest similarity (TOP1) appears between the best similarity threshold (e.g., 55%) and the high similarity threshold (65%), the similarity appears in this interval in less certain When the threshold is at the edge, the probability of misidentification is high.
  • the face recognition comparison yields TOP N above the optimal similarity threshold
  • TOP N detects the corresponding user ID through the face feature vector. If there are multiple feature vectors for the same person, Then, the similarity of multiple face feature vectors of the same person is weighted and accumulated.
  • the similarity between the feature vectors of the photo currently collected by a person and the three face photos is a, b, c (the best similarity is 55%, the high similarity threshold is 65%, 55% ⁇ a, b, c ⁇ 65%), then the similarity between the person ’s current photo and the photo in the face database may be, for example: a + n * b + m * c, where n and m are accumulation coefficients (0 ⁇ n, m ⁇ 1).
  • the output result is re-ranked according to the user ID (each person's unique ID, which can correspond to multiple photos).
  • a second similarity degree comparison is performed on the face image to be recognized and a plurality of second pictures to obtain a plurality of second similarity degrees.
  • the second similarity comparison includes an age comparison and a gender comparison.
  • age comparison may be performed on the face image to be identified with a plurality of second pictures to generate age similarity
  • gender comparison may be performed on the face image to be identified with a plurality of second pictures to generate gender similarity.
  • a facial feature recognition result of the facial image to be recognized is determined according to the plurality of second similarities. For example, based on the gender and age recognition of the current face photo, the TOP ranking is re-ranked based on the real gender and age of each person in the TOP.
  • the true gender and age attributes of each face in the face database can be obtained from the ID number information.
  • the age recognition feature is used to perform secondary recognition on the recognition result, and a way of providing the final recognition result is provided.
  • facial features are quickly and accurately recognized on the face, and the recognition results are output.
  • FIG. 2 is a flowchart of a facial recognition method according to a second embodiment of the present application.
  • the facial feature recognition method 20 shown in FIG. 2 is a detailed description of “when the largest first similarity among a plurality of first similarities is greater than within a first similarity threshold”.
  • a user identifier corresponding to the facial image to be identified in the database is determined according to the recognition result.
  • the face image to be identified is added to the database according to the first number of pictures corresponding to the user identification in the database.
  • the first image in the database is updated with the facial image to be identified according to the number of first pictures corresponding to the user identification in the database.
  • the highest threshold of the first similarity may be, for example, 65%.
  • the face image to be identified can be added to the database and the user ID
  • the same neural network model is used to extract a face feature database, and the new first picture of the user is stored in the database to support one person with multiple faces.
  • the first time of the photos in the first picture corresponding to the user in the database may be more than X years (for example, three years).
  • a picture is deleted, and then the face image to be identified is added to the database under the user's ID, and the same neural network model is used to extract a face feature database, which is stored in the database as a newly added first picture of the user.
  • the facial image to be recognized is not added to the database as the first picture.
  • multiple photos corresponding to each user can cover various scenes at the time of face collection, including light and face size, by means of one user corresponding to multiple photos. Face angle, etc., can provide diverse first pictures for subsequent face recognition.
  • the facial recognition method of the present disclosure in the database, a method of replacing multiple first photos in the database with multiple photos corresponding to each user and avoiding bad facial recognition caused by changes in user age can be avoided. influences.
  • FIG. 3 is a flowchart of a facial recognition method according to a third embodiment of the present application.
  • the process 30 shown in FIG. 3 is a detailed description of S102 in the process shown in FIG. 2 "Comparing a first similarity of a face image to be recognized with multiple first pictures in a database to obtain multiple first similarities" .
  • the feature points are compared with the feature points of multiple first pictures in the database.
  • Face recognition comparison includes three processes: face image preprocessing, feature extraction, and feature comparison.
  • Face preprocessing includes two processes: face detection and face registration.
  • Face comparison is an algorithm that measures the similarity between two faces. The input of the face comparison algorithm is the similarity between the two facial features and the output. The extracted facial feature data is searched and matched with the feature template stored in the database, and an optimal similarity threshold is set. When the similarity exceeds the threshold, the matched result is output.
  • Fig. 4 is a flowchart illustrating a method for facial recognition according to a fourth embodiment of the present application.
  • the process 40 shown in FIG. 4 is a detailed description of S208 in the process shown in FIG. 2 "Comparing a second similarity of a face image to be recognized with a plurality of second pictures to obtain a plurality of second similarities".
  • age comparison is performed on a face image to be recognized and a plurality of second pictures to generate age similarity.
  • the difficulty of face age recognition lies in the recognition of different ages of a single person and the recognition of different ages of multiple people. Face age recognition is often combined with face recognition to more accurately determine whether "is a person within a certain number of years" "The problem.
  • the characteristics of age recognition include, but are not limited to, the position of an adult's eyes is generally 1 ⁇ 2 above and below the head. The distance from the corner of the outer eye to the corner of the mouth is equal to the distance from the tragus. Because of the loss of teeth, the facial features of the elderly are slightly shorter than 1/2 under the eyes. The position of the child's facial features is not yet full because of the chin, and the face above the eyes is slightly longer than 1/2.
  • the process of face data training is: 1. Extracting facial feature points 2. Constructing proportional features, length features and other features 3. Performing cluster classification training based on samples.
  • a first age of the facial image to be identified may be determined; a plurality of second ages of the plurality of second pictures may be determined; and a first distance and a plurality of second ages may be determined by vector distance calculation.
  • Age similarity between ages may be determined.
  • the vector distance calculation includes: Euclidean distance calculation, variance calculation, and cosine distance calculation.
  • a gender comparison is performed on the facial image to be recognized and a plurality of second pictures to generate a gender similarity.
  • Face gender classification is a second-class problem. Two key issues that need to be solved for face gender classification are face feature extraction and the choice of a classifier.
  • the gender-specific facial features include, but are not limited to, male skulls: sharp edges and corners, straight lines, and eyebrow arches that have more prominent forehead slopes than women. The orbits are smaller than women, and the nasal and mandible bones are more developed.
  • Female skull The edges and corners are soft and round. The eye sockets are larger than those of men, and the nose and jaw atrophy, and the entire head shape appears slightly smaller.
  • the gender of the facial image to be recognized may be determined through feature recognition, and the gender is determined based on the identity information of the user corresponding to the second picture, and finally a gender comparison is performed.
  • the second similarity is determined by the age similarity and the gender similarity.
  • the method of facial feature vector extraction in face recognition is to train a deep facial neural network model through a large amount of face sample data.
  • This model is used to extract facial image feature vectors in the face base database, and it is also used in the current Feature extraction of face images collected and detected; face gender and age attribute recognition are also trained by a large number of face images of different genders, ages, and gender classification and age classification.
  • face gender and age attribute recognition are also trained by a large number of face images of different genders, ages, and gender classification and age classification.
  • the neural network used is different from the face recognition feature extraction.
  • supervised learning of face features from two other dimensions through face gender and age recognition can assist in the selection and rearrangement of face recognition results, and has high confidence in the face recognition field.
  • Fig. 5 is a flowchart illustrating a facial recognition method according to a fifth embodiment of the present application. A detailed description of the entire process of facial feature recognition in the facial recognition method 50 shown in FIG. 5 is shown in FIG. 5.
  • S502 an image to be recognized is compared with a first image to obtain a first similarity.
  • the first image corresponding to the highest first similarity is used as the recognition result.
  • the system By presetting a high-precision similarity threshold, for example, if the similarity threshold is 65%, during the comparison search process, the similarity exceeds the high threshold (65%), the current photo is considered to be the person in the face database.
  • the system automatically adds the currently collected faces to the face base database, and uses the same neural network model to extract the face feature database.
  • new faces are stored in the library to support multiple faces of one person, if the number of face photos of that person has reached 10, the most recent face photos that have been older than X years (such as three years) are updated. Otherwise, do not add faces or update face photos; 10 photos per person, as much as possible to cover the scene collected by the current face, including light, face size, face angle, etc.
  • the update of face photos is to avoid Age changes affect recognition.
  • the recognition TOP1 is directly considered to be the target face.
  • the highest similarity (TOP1) appears between the best similarity threshold (e.g. 55%) and the high similarity threshold (65%) (when the similarity appears on the edge of a less certain threshold, the probability of misidentification is very high Large)
  • TOP N above 1, the corresponding user ID is detected by TOF N through the face feature vector. If there are multiple feature vectors, For the same person, the similarity between multiple face feature vectors of the same person is weighted and accumulated.
  • the similarity between the feature vectors of a person's current photo and three face photos is a, b, c (most If the best similarity is 55%, the high similarity threshold is 65%, and 55% ⁇ a, b, c ⁇ 65%), then the similarity between the current photo of the person and the photos in the face database is (including but not limited to) The following calculation method): a + n * b + m * c, where n and m are accumulation coefficients (0 ⁇ n, m ⁇ 1), the similarity of the same person is performed And, re-output according to the user ID (unique per ID, may correspond to multiple pictures) sorting a TOP P, and outputs, this method may improve the recognition rate largely;
  • the feature vectors extracted from the currently collected photos are compared with all face features in the face database.
  • the highest similarity does not exceed the high similarity threshold, appears between the best similarity and high similarity, and multiple photos of the same person.
  • the similarity is merged.
  • the gender and age of the currently collected face image are identified, and the actual real gender of the person corresponding to the face of TOP above all the best similarity thresholds.
  • New vector distance calculation with age (gender and age attributes with a confidence coefficient, vector distance calculation, including but not limited to using Euclidean distance, variance, cosine distance, etc.) to get a new TOP
  • the picture corresponding to the maximum value in TOP M can be used as the recognition result, and for example, all pictures in TOP M can be used as the recognition result, and these pictures can be displayed in the face recognition terminal.
  • artificial means which is not limited in this application.
  • the face recognition method disclosed in the present disclosure is directed to a scene where a current face image collected by a face collection terminal is (1: N) searched and compared in a base library (more than one million) with a large number of face features, and a method is provided.
  • a current face image collected by a face collection terminal is (1: N) searched and compared in a base library (more than one million) with a large number of face features, and a method is provided.
  • a method is provided.
  • the face recognition method of the present disclosure is a method for significantly improving the accuracy rate of face recognition in a face recognition large database search scenario.
  • the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, or an optical disk.
  • Fig. 6 is a functional block diagram of a facial recognition device according to a sixth embodiment of the present application.
  • the facial recognition device 60 includes a first comparison module 602, a threshold module 604, a second picture generation module 606, a second comparison module 608, and a first result module 610.
  • the first comparison module 602 is configured to perform a first similarity comparison between a face image to be identified and a plurality of first pictures in a database to obtain a plurality of first similarities; wherein the first similarity comparison is a facial feature comparison.
  • the threshold module 604 is configured to extract a portion of the first similarity among the plurality of first similarities when the largest first similarity among the plurality of first similarities is within the first similarity threshold; the first similarity threshold may be within For example, 55% -65%.
  • the second picture generating module 606 is configured to determine a plurality of second pictures by using a plurality of first pictures corresponding to a part of the first similarity; for example, combining the first pictures having the same user ID; The second picture and corresponding first similarities.
  • the second comparison module 608 is configured to perform a second similarity comparison between the face image to be identified and a plurality of second pictures to obtain a plurality of second similarities; wherein the second similarity comparison includes an age comparison and a gender comparison.
  • the first result module 610 is configured to determine a facial feature recognition result of the facial image to be recognized according to the multiple second similarities.
  • the age recognition feature is used to perform secondary recognition on the recognition result, and a way of providing a final recognition result can be provided in a large amount.
  • facial features are quickly and accurately recognized on the face, and the recognition results are output.
  • Fig. 7 is a block diagram of a facial recognition device according to a seventh embodiment of the present application.
  • the facial recognition device 70 further includes a second result module 702, a user identification module 704, and a picture update module 706.
  • the second result module 702 is configured to use the first picture corresponding to the largest first similarity as the recognition of the facial image to be recognized when the largest first similarity among the plurality of first similarities is greater than the first similarity threshold. result.
  • the user identification module 704 is configured to determine a user identification corresponding to a facial image to be identified in a database according to the recognition result;
  • the picture update module 706 is configured to determine a subsequent processing mode according to the first picture quantity corresponding to the user identifier in the database.
  • FIG. 8 is a schematic structural diagram of an electronic device according to an eighth embodiment of the present application.
  • FIG. 8 An electronic device 200 according to this embodiment of the present disclosure is described below with reference to FIG. 8.
  • the electronic device 200 shown in FIG. 8 is merely an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.
  • the electronic device 200 is expressed in the form of a general-purpose computing device.
  • the components of the electronic device 200 may include, but are not limited to, at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including the storage unit 220 and the processing unit 210), a display unit 240, and the like.
  • the storage unit stores program code, and the program code can be executed by the processing unit 210, so that the processing unit 210 executes various exemplary embodiments according to the present disclosure described in the above-mentioned electronic prescription circulation processing method section of this specification. Steps of the implementation.
  • the processing unit 210 may perform the steps shown in FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5.
  • the storage unit 220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 2201 and / or a cache storage unit 2202, and may further include a read-only storage unit (ROM) 2203.
  • RAM random access storage unit
  • ROM read-only storage unit
  • the storage unit 220 may further include a program / utility tool 2204 having a group (at least one) of program modules 2205.
  • program modules 2205 include, but are not limited to, an operating system, one or more application programs, other program modules, and programs. Data, each or some combination of these examples may include an implementation of the network environment.
  • the bus 230 may be one or more of several types of bus structures, including a memory unit bus or a memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any bus structure in a variety of bus structures bus.
  • the electronic device 200 may also communicate with one or more external devices 300 (such as a keyboard, pointing device, Bluetooth device, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 200, and / or with Any device (eg, router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. This communication may be performed through an input / output (I / O) interface 250.
  • the electronic device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 260.
  • the network adapter 260 may communicate with other modules of the electronic device 200 through the bus 230.
  • the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network Including several instructions to cause a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to an embodiment of the present disclosure.
  • a non-volatile storage medium which may be a CD-ROM, a U disk, a mobile hard disk, etc.
  • a computing device which may be a personal computer, a server, or a network device, etc.
  • An embodiment of the present application further provides a computer program product 400.
  • the computer program product 400 includes a computing program stored on a non-volatile computer-readable storage medium.
  • the computer program includes program instructions. When the program instructions are executed by a computer, The computer is caused to execute the above-mentioned robot control method based on the optical fiber communication network.
  • Fig. 9 is a schematic structural diagram of a computer-readable storage medium according to a ninth embodiment of the present application.
  • a program product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may adopt a portable compact disc read-only memory (CD-ROM) and include program code, and may be implemented in a terminal device. For example running on a personal computer.
  • the program product of the present disclosure is not limited thereto.
  • the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, which include object-oriented programming languages such as Java, C ++, and the like, as well as conventional procedural Programming language—such as "C" or a similar programming language.
  • the program code may be executed entirely on the user computing device, partly on the user device, as an independent software package, partly on the user computing device, partly on the remote computing device, or entirely on the remote computing device or server On.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g. (Commercially connected via the Internet).
  • LAN local area network
  • WAN wide area network
  • an external computing device e.g. (Commercially connected via the Internet).
  • the computer-readable medium carries one or more programs, and when the one or more programs are executed by one of the devices, the computer-readable medium implements the following functions: a face image to be identified and a plurality of first
  • the pictures are compared for the first similarity to obtain multiple first similarities.
  • a part of the multiple first similarities is extracted.
  • a similarity determining a plurality of second pictures by using a plurality of first pictures corresponding to a portion of the first similarity; comparing a face image to be recognized with a plurality of second pictures to obtain a plurality of second similarities ; And determining a facial feature recognition result of the facial image to be recognized according to the plurality of second similarities.
  • modules in the above embodiments may be distributed in the device according to the description of the embodiment, or corresponding changes may be made in one or more devices that are different from this embodiment.
  • the modules in the above embodiments may be combined into one module, or further divided into multiple sub-modules.
  • the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network Including instructions to cause a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a non-volatile storage medium which may be a CD-ROM, a U disk, a mobile hard disk, etc.
  • a computing device which may be a personal computer, a server, a mobile terminal, or a network device, etc.

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Abstract

La présente invention concerne un procédé et un appareil de reconnaissance faciale, un dispositif électronique et un support lisible par ordinateur. Ledit procédé consiste à : effectuer une première comparaison de similarité entre une pluralité de premières images dans une base de données et une image faciale à reconnaître, afin d'obtenir une pluralité de premières similarités ; lorsqu'une première similarité maximale parmi la pluralité de premières similarités est comprise dans un premier seuil de similarité, extraire une partie de la pluralité de premières similarités ; déterminer une pluralité de secondes images au moyen de la pluralité de premières images correspondant à la partie de la pluralité de premières similarités ; réaliser une seconde comparaison de similarité entre ladite image faciale et la pluralité de secondes images, afin d'obtenir une pluralité de secondes similarités ; et déterminer un résultat de reconnaissance de caractéristiques faciales de ladite image faciale en fonction de la pluralité de secondes similarités. Le procédé et l'appareil de reconnaissance faciale, le dispositif électronique et le support lisible par ordinateur de la présente invention peuvent reconnaître rapidement et avec précision des caractéristiques faciales d'un visage humain à partir d'une grande quantité de données et délivrer un résultat de reconnaissance.
PCT/CN2019/095286 2018-08-24 2019-07-09 Procédé et appareil de reconnaissance faciale, dispositif électronique et support lisible par ordinateur WO2020038136A1 (fr)

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