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WO2021036436A1 - Procédé et appareil de reconnaissance faciale - Google Patents

Procédé et appareil de reconnaissance faciale Download PDF

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Publication number
WO2021036436A1
WO2021036436A1 PCT/CN2020/096992 CN2020096992W WO2021036436A1 WO 2021036436 A1 WO2021036436 A1 WO 2021036436A1 CN 2020096992 W CN2020096992 W CN 2020096992W WO 2021036436 A1 WO2021036436 A1 WO 2021036436A1
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Prior art keywords
image
recognized
face
compared
features
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Ceased
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PCT/CN2020/096992
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English (en)
Chinese (zh)
Inventor
韩雨
杭欣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suning Cloud Computing Co Ltd
Suningcom Group Co Ltd
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Suning Cloud Computing Co Ltd
Suningcom Group Co Ltd
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Priority to CA3152812A priority Critical patent/CA3152812A1/fr
Publication of WO2021036436A1 publication Critical patent/WO2021036436A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/168—Feature extraction; Face representation

Definitions

  • the present invention relates to the technical field of computer vision, in particular to a face recognition method and device.
  • Face recognition is a kind of biometric recognition technology based on human facial feature information.
  • a series of related technologies that use a video camera or camera to collect images or video streams containing human faces, and automatically detect and track human faces in the images, and then recognize the detected human faces, usually also called face recognition and facial recognition.
  • face recognition technology has become one of the hottest applications of artificial intelligence, such as swiping face to board plane, swiping face to get toilet paper, swiping face to pay, swiping face to check attendance, swiping face to recognize pedestrians running red lights, and so on.
  • Face recognition usually has the following three application modes:
  • the 1:1 mode also known as the identity verification mode, is essentially a process in which the computer quickly compares the current face with the portrait database and finds whether it matches. It can be simply understood as proving that you are you. "Face-swiping" boarding, ticket checking, and payment all belong to the 1:1 verification;
  • the 1 to N mode is to find the current user's face data (ie the image to be recognized) in a massive portrait database and perform matching. People who commit abductions and expose red lights are all classified as 1:N face recognition, that is, one target is found out of N faces;
  • the M-to-N mode is a process of facial recognition of all people in the scene through the computer and comparison with the portrait database. It is a dynamic face comparison, which can be fully applied to a variety of scenarios, such as public security, welcoming, and Robot applications, etc.
  • the factors that affect the accuracy of face recognition include:
  • the embodiments of the present invention provide a face recognition method and device to overcome the face recognition caused by factors such as the increase in the number of comparisons and the increase in the number of registered base IDs in the prior art. Problems such as low accuracy.
  • the technical solution adopted by the present invention is:
  • a face recognition method includes the following steps:
  • each ID in the comparison library includes multiple reference images with different poses in different scenarios;
  • the facial features of the image to be identified and all features of the reference image to be compared it is determined whether the image to be identified matches the reference image to be compared.
  • the acquiring the facial features of the image to be recognized according to the image to be recognized includes:
  • the identification request when the identification request is a 1:1 identification request, the identification request includes the ID of the reference image to be compared;
  • the obtaining all the features of the reference image to be compared from the comparison library according to the recognition request includes:
  • the judging whether the image to be identified matches the reference image to be compared according to the facial features of the image to be identified and all the features of the reference image to be compared includes:
  • the reference images to be compared are all reference images under all IDs in the comparison library
  • the obtaining all the features of the reference image to be compared from the comparison library according to the recognition request includes:
  • the judging whether the image to be identified matches the reference image to be compared according to the facial features of the image to be identified and all the features of the reference image to be compared includes:
  • a face recognition device in another aspect, includes:
  • the data acquisition module is used to acquire the recognition request and the image to be recognized
  • the first feature acquisition module is configured to acquire the facial features of the image to be identified according to the image to be identified;
  • the second feature acquisition module is configured to acquire all the features of the reference image to be compared from the comparison library according to the recognition request, and each ID in the comparison library includes references of different poses in multiple different scenarios image;
  • the image recognition module is configured to determine whether the image to be recognized matches the reference image to be compared based on the facial features of the image to be recognized and all the features of the reference image to be compared.
  • the first feature acquisition module includes:
  • An image detection unit configured to perform face frame detection and face key point detection on the to-be-recognized image, and obtain a face image and key point positions corresponding to the to-be-recognized image;
  • a normalization processing unit configured to perform normalization processing on the face image according to the position of the key point to obtain a processed face image
  • the feature extraction unit is configured to perform feature extraction on the processed face image, and obtain the face feature corresponding to the image to be recognized.
  • the identification request when the identification request is a 1:1 identification request, the identification request includes the ID of the reference image to be compared;
  • the second feature acquisition module is specifically used for:
  • the image recognition module includes:
  • the first calculation unit is configured to calculate each reference image and the image to be recognized according to all the features of each reference image under the ID of the reference image to be compared and the facial features of the image to be recognized The similarity;
  • a first comparison unit configured to compare the similarity with a first preset threshold, and if the similarity is greater than the first preset threshold, determine that the reference image matches the image to be recognized successfully;
  • the second comparison unit is configured to obtain the number of reference images that are successfully matched with the image to be identified, and if the number exceeds half of the total number of the reference images, determine that the image to be identified is compared with the image to be identified The ID of the reference image matches successfully.
  • the reference images to be compared are all reference images under all IDs in the comparison library
  • the second feature acquisition module is also used for:
  • the image recognition module further includes:
  • the second calculation unit is configured to calculate and obtain the similarity between the image to be recognized and each ID according to all the features of each reference image under each ID and the facial features of the image to be recognized;
  • the third comparison unit is used to determine whether the similarity of the ID with the highest similarity value meets the second preset threshold, and if so, it is determined that the image to be recognized matches the ID with the highest similarity value, otherwise it is determined that the The image to be recognized is an unregistered image.
  • the face recognition method and device provided by the embodiments of the present invention use a comparison library of reference images of different poses in multiple different scenarios with the same ID as the matching standard, which improves the accuracy of recognition and enhances the robustness of the algorithm It has good adaptability to the facial expression and picture quality of the recognized image;
  • the face recognition method and device provided by the embodiments of the present invention adopt the scheme of detecting the face frame and the key points of the face together, which can not only accurately locate the face position, but also reduce the steps and time in the recognition process. , Improve the efficiency of recognition;
  • the face recognition method and device provided by the embodiments of the present invention select the ID with the highest similarity by first calculating the similarity between each reference image under each ID and the image to be recognized in a 1:N recognition scenario , And then determine whether the similarity meets the second preset threshold to match the ID matching the image to be recognized, which improves the anti-attack ability of the algorithm.
  • Fig. 1 is a flowchart showing a face recognition method according to an exemplary embodiment
  • Fig. 2 is a flow chart showing obtaining facial features of an image to be recognized according to an image to be recognized according to an exemplary embodiment
  • Fig. 3 shows according to an exemplary embodiment in the 1:1 mode, according to the facial features of the image to be recognized and all the features of the reference image to be compared, it is determined whether the image to be recognized matches the reference image to be compared Flow chart
  • Fig. 4 shows according to an exemplary embodiment in the 1 to N mode, according to the facial features of the image to be recognized and all the features of the reference image to be compared, it is determined whether the image to be recognized matches the reference image to be compared Flow chart
  • Fig. 5 is a schematic structural diagram of a face recognition device according to an exemplary embodiment.
  • Facial features are the most suitable type of biological features for identification. Compared with fingerprints, iris and other features, they have the advantages of simple collection, low cost, and easy identification.
  • the use of human face for identity recognition has a wide range of applications in many scenarios such as face-swiping login, face-swiping credit investigation, and security verification. On the one hand, face recognition reduces manual operations and can save costs. On the other hand, it facilitates identity verification and improves user experience.
  • the basic process of face recognition is to extract features from the face image to be recognized, and then compare with the face features registered in the database.
  • Fig. 1 is a flowchart of a face recognition method according to an exemplary embodiment. Referring to Fig. 1, the method includes the following steps:
  • the image to be recognized is collected from the photo, video or camera, and the corresponding recognition request is obtained.
  • face recognition includes three modes: 1:1 mode, 1:N mode, and M:N mode.
  • the face recognition method provided by the embodiment of the present invention is mainly applicable to 1:1 recognition scenarios and 1:N recognition scenarios. Therefore, the recognition request in the embodiment of the present invention mainly includes a 1:1 recognition request or a 1:1 recognition request. N identifies the request.
  • 1:1 matching mainly solves the problem of determining whether the image to be recognized and the reference image belong to the same person
  • 1:N matching mainly solves the problem of determining which person the image to be recognized belongs to.
  • S2 Acquire the facial features of the image to be recognized according to the image to be recognized.
  • the facial features of the image to be recognized need to be extracted from the image to be recognized.
  • a convolutional neural network may be used in the embodiment of the present invention to extract the facial features of the image to be recognized.
  • the facial features of the image that is, the depth features of the image to be recognized).
  • the identification request in the embodiment of the present invention mainly includes a 1:1 identification request or a 1:N identification request.
  • a method of comparing the facial features of the image to be recognized with the features of the reference image to be compared is adopted. Since different recognition requests use different reference images to be compared, it is also necessary to obtain all the features of the corresponding reference images to be compared from the comparison library according to the recognition request.
  • Each ID in the comparison library includes multiple reference images with different poses in different scenarios to ensure the diversity of the comparison library, thereby increasing the probability of successful recognition.
  • the reference image maintained under each ID in the comparison library mainly includes the following three scenarios:
  • the ID photos uploaded by the company or group members are authorized to be used for internal personnel authentication, and can be used as a reference image for a class of scenes.
  • the face-scanning login function of social software will ask for the user's authorization and use the avatar for personal identity authentication.
  • the user is asked to nod or shake his head.
  • the camera will check the action, and automatically collect the right and left deflection facial image and store it under the corresponding ID as a reference image.
  • the reference image when the reference image is collected, it must meet the following quality screening standards: meet the multi-dimensional thresholds of sharpness, angle and facial features, and the selected reference image should take into account the face and small deflection.
  • the profile of the face is used to increase the probability of matching successfully with the image to be recognized in a variety of poses.
  • the reference image in the comparison library is collected through a 1:1 port, when registering to the comparison library, enter a video of the registrant, and use the quality algorithm to obtain the best quality from the video. Feature extraction of several photos is registered and stored. If it is collected through a 1:N port, after collecting the photos to be recognized, compare them with the existing reference images in the comparison library. If the similarity of the closest reference image is also lower than the set threshold, then Considering that there is no reference image corresponding to the ID in the base library, track the person in the image to be recognized, collect a video, use a quality algorithm to select several pictures with the best quality from the video, and create a new ID to add to the registration library.
  • S4 Determine whether the image to be identified matches the reference image to be compared according to the facial features of the image to be identified and all features of the reference image to be compared.
  • the similarity between the image to be identified and the reference image to be compared is calculated based on the facial features of the image to be identified and all the features of the reference image to be compared, and then the image to be identified and the reference image to be compared are judged based on the similarity. Whether the reference image matches.
  • Fig. 2 is a flow chart showing the acquisition of facial features of the image to be recognized according to the image to be recognized according to an exemplary embodiment.
  • the Obtaining the facial features of the image to be recognized according to the image to be recognized includes:
  • S2.1 Perform face frame detection and face key point detection on the image to be recognized, and obtain the face image and key point positions corresponding to the image to be recognized.
  • performing face frame detection on the image to be recognized refers to detecting and locating the face in the image to be recognized, returning high-precision face frame coordinates, and intercepting the face image in the image to be recognized according to the coordinates.
  • the key points of the face are detected on the image to be recognized, and the key areas of the face are located, including the key points of the eyes, nose, and mouth.
  • the detected face images in the image to be recognized include one or more, and the user can decide whether to recognize a single face frame or a multi-person face frame according to the actual application scenario, which is not limited in the embodiment of the present invention.
  • S2.2 Perform normalization processing on the face image according to the position of the key point, and obtain a processed face image.
  • Face normalization includes two aspects: one is geometric normalization, and the other is gray normalization. Geometric normalization is also called position calibration. It will help correct size differences and angle tilts caused by changes in imaging distance and face pose, and can solve the problems of face scale changes and face rotation. Specifically, it includes three links: normalization of face scale, flat face rotation correction (tilted head), and deep face rotation correction (face twisting). For some highly demanding deep face rotation correction, a 3D model of the face can be used.
  • Gray normalization is used to compensate the face image obtained under different light intensities and light source directions, so as to reduce the change of the image signal caused by the change of light alone. It should be noted here that, in order to facilitate subsequent use of models (such as convolutional neural networks) to extract facial features, in the embodiment of the present invention, the facial image needs to be adjusted to a size suitable for the input model.
  • S2.3 Perform feature extraction on the processed face image, and obtain the face feature corresponding to the image to be recognized.
  • a pre-trained convolutional neural network is used to perform feature extraction on the face image processed by the above steps to obtain the face feature corresponding to the image to be recognized.
  • the identification request when the identification request is a 1:1 identification request, the identification request includes the ID of the reference image to be compared;
  • the obtaining all the features of the reference image to be compared from the comparison library according to the recognition request includes:
  • the acquired recognition request is a 1:1 recognition request.
  • the recognition request includes the recognition request.
  • the ID of the compared reference image At this time, according to the ID of the reference image to be compared, all the features of all the reference images under the ID are obtained from the comparison library.
  • Fig. 3 is a flow chart showing whether the image to be recognized matches the reference image to be compared according to the facial features of the image to be recognized and all the features of the reference image to be compared according to an exemplary embodiment, refer to Fig. 3 As shown, as a preferred embodiment, in the embodiment of the present invention, the judgment is made based on the facial features of the image to be recognized and all the features of the reference image to be compared. Whether the reference image to be compared matches or not includes:
  • S401 Calculate the similarity between each reference image and the image to be recognized according to all the features of each reference image under the ID of the reference image to be compared and the facial features of the image to be recognized.
  • each reference image under the ID of the reference image to be compared needs to be compared with the image to be recognized.
  • the similarity between each reference image and the image to be recognized can be calculated based on all the features of each reference image under the ID of the reference image to be compared and the facial features of the image to be recognized, and the image can be determined based on the similarity. Comparison.
  • S402 Compare the similarity with a first preset threshold, and if the similarity is greater than the first preset threshold, determine that the reference image matches the image to be recognized successfully.
  • the first preset threshold may be set according to actual requirements, and the specific value of the first preset threshold is not limited here.
  • the similarity between the reference image and the image to be recognized exceeds (that is, greater than) the first preset threshold, it is determined that the reference image is successfully matched with the image to be recognized.
  • S403 Acquire the number of reference images that are successfully matched with the image to be recognized, and if the number exceeds half of the total number of reference images, determine the ID of the image to be recognized and the reference image to be compared The match is successful.
  • the number of reference images successfully matched with the image to be recognized under the ID of the reference image to be compared exceeds the total number of reference images participating in the matching (here refers to the total number of reference images under the ID of the reference image to be compared )
  • it is finally determined that the ID matching of the image to be identified and the reference image to be compared is successful; otherwise, it is determined that the ID matching of the image to be identified and the reference image to be compared is unsuccessful.
  • This setting can avoid the low similarity between the reference image with the same ID and the single image to be recognized due to large differences in scenes, occlusions, etc., and it can also shield the reference image with different IDs and the image to be recognized due to accident
  • the problem of high similarity caused by factors can improve the accuracy of recognition.
  • the reference images to be compared are all reference images under all IDs in the comparison library ;
  • the acquiring all the features of the reference image to be compared from the comparison library according to the recognition request includes:
  • the obtained recognition request is a 1:N recognition request.
  • the reference image to be compared Compare all reference images under all IDs in the library. At this time, all features of all reference images under all IDs need to be obtained from the comparison library.
  • Fig. 4 shows according to an exemplary embodiment in the 1 to N mode, according to the facial features of the image to be recognized and all the features of the reference image to be compared, it is determined whether the image to be recognized matches the reference image to be compared 4, as a preferred embodiment, in an embodiment of the present invention, according to the facial features of the image to be recognized and all features of the reference image to be compared, Determining whether the image to be recognized matches the reference image to be compared includes:
  • S501 Calculate and obtain the similarity between the image to be recognized and each ID according to all the features of each reference image under each ID and the facial features of the image to be recognized.
  • the face recognition mode is a 1:N mode
  • all reference images under all IDs in the comparison library need to be compared with the image to be recognized.
  • the similarity between each reference image and the image to be recognized can be calculated based on all the features of each reference image under each ID and the facial features of the image to be recognized, and the image comparison can be performed based on the similarity.
  • S502 Determine whether the similarity of the ID with the highest similarity value meets the second preset threshold, if so, determine that the image to be recognized matches the ID with the highest similarity value; otherwise, determine that the image to be recognized is unidentified. Register the image.
  • the second preset threshold can be set according to actual needs.
  • the specific value of the second preset threshold is not limited here, and the second preset threshold is set to shield the attack of unregistered ID photos.
  • the image to be recognized that is unsuccessfully matched with the reference image to be compared is registered with the comparison library, a new ID is generated, and the The image to be recognized is stored in the new ID as a reference image under the new ID.
  • Fig. 5 is a schematic structural diagram of a face recognition device according to an exemplary embodiment. Referring to Fig. 5, the device includes:
  • the data acquisition module is used to acquire the recognition request and the image to be recognized
  • the first feature acquisition module is configured to acquire the facial features of the image to be identified according to the image to be identified;
  • the second feature acquisition module is configured to acquire all the features of the reference image to be compared from the comparison library according to the recognition request, and each ID in the comparison library includes references of different poses in multiple different scenarios image;
  • the image recognition module is configured to determine whether the image to be recognized matches the reference image to be compared based on the facial features of the image to be recognized and all the features of the reference image to be compared.
  • the first feature acquisition module includes:
  • An image detection unit configured to perform face frame detection and face key point detection on the to-be-recognized image, and obtain a face image and key point positions corresponding to the to-be-recognized image;
  • a normalization processing unit configured to perform normalization processing on the face image according to the position of the key point to obtain a processed face image
  • the feature extraction unit is configured to perform feature extraction on the processed face image, and obtain the face feature corresponding to the image to be recognized.
  • the identification request when the identification request is a 1:1 identification request, the identification request includes the ID of the reference image to be compared;
  • the second feature acquisition module is specifically used for:
  • the image recognition module includes:
  • the first calculation unit is configured to calculate each reference image and the image to be recognized according to all the features of each reference image under the ID of the reference image to be compared and the facial features of the image to be recognized The similarity;
  • a first comparison unit configured to compare the similarity with a first preset threshold, and if the similarity is greater than the first preset threshold, determine that the reference image matches the image to be recognized successfully;
  • the second comparison unit is configured to obtain the number of reference images that are successfully matched with the image to be identified, and if the number exceeds half of the total number of the reference images, determine that the image to be identified is compared with the image to be identified The ID of the reference image matches successfully.
  • the reference images to be compared are all reference images under all IDs in the comparison library ;
  • the second feature acquisition module is also used for:
  • the image recognition module further includes:
  • the second calculation unit is configured to calculate and obtain the similarity between the image to be recognized and each ID according to all the features of each reference image under each ID and the facial features of the image to be recognized;
  • the third comparison unit is used to determine whether the similarity of the ID with the highest similarity value meets the second preset threshold, and if so, it is determined that the image to be recognized matches the ID with the highest similarity value, otherwise it is determined that the The image to be recognized is an unregistered image.
  • the face recognition method and device provided by the embodiments of the present invention use a comparison library of reference images of different poses in multiple different scenarios with the same ID as the matching standard, which improves the accuracy of recognition and enhances the robustness of the algorithm It has good adaptability to the facial expression and picture quality of the recognized image;
  • the face recognition method and device provided by the embodiments of the present invention adopt the scheme of detecting the face frame and the key points of the face together, which can not only accurately locate the face position, but also reduce the steps and time in the recognition process. , Improve the efficiency of recognition;
  • the face recognition method and device provided by the embodiments of the present invention select the ID with the highest similarity by first calculating the similarity between each reference image under each ID and the image to be recognized in a 1:N recognition scenario , And then determine whether the similarity meets the second preset threshold to match the ID matching the image to be recognized, which improves the anti-attack ability of the algorithm.
  • the face recognition device provided in the above embodiment triggers the face recognition service
  • only the division of the above functional modules is used as an example for illustration.
  • the above functions can be allocated to different functions according to needs.
  • Module completion that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.
  • the face recognition device provided in the above embodiment and the face recognition method embodiment belong to the same concept, that is, the device is based on the face recognition method.
  • the specific implementation process please refer to the method embodiment, which will not be repeated here.
  • the program can be stored in a computer-readable storage medium.
  • the storage medium mentioned can be a read-only memory, a magnetic disk or an optical disk, etc.

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Abstract

La présente invention concerne un procédé et un appareil de reconnaissance faciale. Le procédé consiste à : acquérir une demande de reconnaissance et une image à reconnaître ; en fonction de l'image à reconnaître, acquérir une caractéristique faciale de l'image à reconnaître ; en fonction de la demande de reconnaissance, acquérir, à partir d'une bibliothèque de comparaisons, toutes les caractéristiques d'une image de référence pour comparaison, chaque ID de la bibliothèque de comparaisons comprenant des images de référence de différentes postures dans une pluralité de scénarios différents ; et en fonction de la caractéristique faciale de l'image à reconnaître, et de toutes les caractéristiques de l'image de référence pour comparaison, déterminer si l'image à reconnaître correspond à l'image de référence pour comparaison. Selon la présente invention, à l'aide de la bibliothèque de comparaisons d'images de référence, du même ID, de différentes postures dans une pluralité de scénarios différents en tant que norme de mise en correspondance, la précision de la reconnaissance est améliorée, la robustesse d'un algorithme est améliorée, et une meilleure adaptabilité à l'expression, à la qualité d'image, etc. d'une image à reconnaître est fournie.
PCT/CN2020/096992 2019-08-26 2020-06-19 Procédé et appareil de reconnaissance faciale Ceased WO2021036436A1 (fr)

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