WO2019196308A1 - Dispositif et procédé de génération de modèle de reconnaissance faciale, et support d'informations lisible par ordinateur - Google Patents

Dispositif et procédé de génération de modèle de reconnaissance faciale, et support d'informations lisible par ordinateur Download PDF

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WO2019196308A1
WO2019196308A1 PCT/CN2018/102401 CN2018102401W WO2019196308A1 WO 2019196308 A1 WO2019196308 A1 WO 2019196308A1 CN 2018102401 W CN2018102401 W CN 2018102401W WO 2019196308 A1 WO2019196308 A1 WO 2019196308A1
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feature point
matching
point pairs
dimensional coordinates
preset
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PCT/CN2018/102401
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English (en)
Chinese (zh)
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王义文
王健宗
肖京
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平安科技（深圳）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
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/757—Matching configurations of points or features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G06V40/172—Classification, e.g. identification

Definitions

  • the present application relates to the field of face recognition technologies, and in particular, to a device, a method, and a computer readable storage medium for generating a face recognition model.
  • Face recognition is a biometric recognition technology based on human facial feature information for identification.
  • the face recognition technology is a two-dimensional face recognition.
  • the two-dimensional face recognition usually uses a camera or a camera to collect a two-dimensional image containing a human face, and detects and tracks the face in the two-dimensional image, and further The detected face is identified.
  • a two-dimensional face image is acquired for recognition, it is easily affected by changes in non-geometric appearance such as posture, expression, illumination, and facial makeup, resulting in low accuracy of face recognition.
  • the present application provides a device, a method, and a computer readable storage medium for generating a face recognition model, the main purpose of which is to improve the accuracy of face recognition.
  • the present application provides a device for generating a face recognition model, the device comprising a memory and a processor, wherein the memory stores a model generation program executable on the processor, the model generation program The following steps are implemented when executed by the processor:
  • A1. Collecting a plurality of facial images of a user photographed from a plurality of perspectives, and acquiring camera parameters for capturing the plurality of facial images;
  • the acquired multiple face images are matched by two to two, and the matched feature point pairs are obtained, and the matched feature point pairs are filtered by a preset feature point screening algorithm to delete the matching. Wrong feature point pairs and obtain two-dimensional coordinates matching the correct feature point pairs on the face image;
  • A3 calculating, according to the two-dimensional coordinates and the camera parameter, matching the corresponding three-dimensional coordinates of the feature point pair, and constructing the three-dimensional point cloud data of the user face according to the calculated three-dimensional coordinates;
  • A4. Convert the three-dimensional point cloud data into a depth image, and use any one of the plurality of facial images as a color image of the user's face;
  • steps A1 to A4 to obtain a preset number of depth images and color images of the user, and use the depth image and the color image as inputs of a preset two-channel convolutional neural network model to train the pair.
  • the channel convolutional neural network model determines model parameters
  • a two-channel convolutional neural network model that determines model parameters is used as a face recognition model, wherein the two-channel convolutional neural network model takes the results of the fully connected layer as an output.
  • the present application further provides a method for generating a face recognition model, the method comprising:
  • B2 Matching the acquired plurality of facial images according to a preset feature matching algorithm to obtain matching feature point pairs, and filtering the matched feature point pairs by a preset feature point screening algorithm to delete the matching Wrong feature point pairs and obtain two-dimensional coordinates matching the correct feature point pairs on the face image;
  • step B1 to step B4 are repeatedly performed to obtain a preset number of depth images and color images of the user, and the depth image and the color image are used as inputs of a preset two-channel convolutional neural network model, and the pair is trained.
  • the channel convolutional neural network model determines model parameters
  • a two-channel convolutional neural network model that determines model parameters is used as a face recognition model, wherein the two-channel convolutional neural network model takes the results of the fully connected layer as an output.
  • the present application further provides a computer readable storage medium having a model generation program stored thereon, the model generation program being executable by one or more processors to implement The steps of the method of generating a face recognition model as described above.
  • FIG. 1 is a schematic diagram of a preferred embodiment of a device for generating a face recognition model of the present applicant
  • FIG. 2 is a flow chart of a preferred embodiment of a method for generating a face recognition model of the present applicant.
  • the application provides a device for generating a face recognition model.
  • FIG. 1 a schematic diagram of a preferred embodiment of a device for generating a face recognition model of the present invention is shown.
  • the face recognition model generating device 1 may be a PC (Personal Computer), or may be a terminal device such as a smart phone, a tablet computer, or a portable computer.
  • PC Personal Computer
  • terminal device such as a smart phone, a tablet computer, or a portable computer.
  • the face recognition model generating apparatus 1 includes at least a memory 11, a processor 12, a communication bus 13, and a network interface 14.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like.
  • the memory 11 may be an internal storage unit of the face recognition model generating device 1 in some embodiments, such as a hard disk of the face recognition model generating device 1.
  • the memory 11 may also be an external storage device of the face recognition model generating device 1 in other embodiments, such as a plug-in hard disk equipped with a face recognition model generating device 1 and a smart memory card (Smart Media Card, SMC). ), Secure Digital (SD) card, Flash Card, etc.
  • the memory 11 may also include an internal storage unit of the generating device 1 of the face recognition model and an external storage device.
  • the memory 11 can be used not only for storing application software of the face recognition model generating device 1 and various types of data, such as code of the model generation program 01, but also for temporarily storing data that has been output or is to be output.
  • the processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing stored in the memory 11.
  • Data such as execution model generation program 01 and the like.
  • Communication bus 13 is used to implement connection communication between these components.
  • the network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device 1 and other electronic devices.
  • a standard wired interface such as a WI-FI interface
  • Figure 1 shows only the face recognition model generation device 1 with components 11-14 and model generation program 01, but it should be understood that not all illustrated components may be implemented, alternative implementations may be more or more Less components.
  • the device 1 may further include a user interface
  • the user interface may include a display
  • an input unit such as a keyboard
  • the optional user interface may further include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display may also be appropriately referred to as a display screen or display unit for displaying information processed in the face recognition model generating device 1 and a user interface for displaying visualization.
  • model generation program 01 is stored in the memory 11; when the processor 12 executes the model generation program 01 stored in the memory 11, the following steps are implemented:
  • A1 Acquire a plurality of facial images of a user photographed from a plurality of angles of view, and acquire camera parameters for capturing the plurality of facial images.
  • the acquired multiple face images are matched by two to two, and the matched feature point pairs are obtained, and the matched feature point pairs are filtered by a preset feature point screening algorithm to delete the matching. Wrong feature point pairs and get the 2D coordinates of the correct feature point pair on the face image.
  • two cameras set at different viewing angles are set to capture the facial image of the user, and two facial images of different angles are obtained.
  • the relative positional relationship between the two cameras and the effective focal length of the camera are known.
  • Feature point matching is performed on two facial images according to a preset feature matching algorithm.
  • the preset feature matching algorithm may be an ORB (Oriented FAST and Rotated BRIEF) algorithm, and the ORB algorithm is a fast feature point extraction and description.
  • the algorithm can detect and match the feature points of the two facial images to find matching feature point pairs in the two images.
  • a SIFT (Scale-invariant feature transform) algorithm may be used to perform feature pair calculation.
  • the number of feature point pairs matched by these feature matching algorithms is small, and there may be a certain amount of feature points matching errors. Therefore, after the feature matching is completed, the acquired feature point pairs need to be filtered to delete the feature point pairs that match the errors, and the accuracy of the face recognition is improved.
  • step A2 may include the following refinement steps:
  • the screening is stopped, and the two-dimensional coordinates of the matching feature point pair on the facial image are obtained; if not, the matching feature point pair is further obtained according to the feature matching algorithm, and the feature point filtering algorithm is obtained according to the feature point matching algorithm.
  • the matched feature point pairs are filtered until the number of matching feature point pairs is greater than the first preset threshold.
  • the preset feature point screening algorithm is specifically as follows: the acquired facial image is divided into K ⁇ K grid regions, wherein the size of the K value may be determined according to pixels of the collected facial image, for example, a photo The size is 1600 ⁇ 1600, then the K value can be set to 80, then there are 20 ⁇ 20 pixel points in each grid area, wherein one feature point corresponds to one pixel point, and the matching in each grid area is counted.
  • the number of feature point pairs that the feature points match in their L ⁇ L neighborhood, preferably, in one embodiment, L 3, then the neighborhood of a grid area is the other 8 networks adjacent to it. Grid area. Based on the principle of motion smoothness, there must be more matching feature points in the neighborhood of the matched feature points.
  • the feature point pairs matched according to the preset feature matching algorithm are counted to determine whether the matching result is correct. If the number of statistical feature point pairs is smaller than the second preset threshold in the neighborhood of a feature point, the feature point is determined to be a pair of feature points that match the error.
  • the statistical feature point pair If a feature point is in the neighborhood of the feature point, the statistical feature point pair If the number is greater than or less than the second predetermined threshold, then the feature point is determined to be the correct feature point pair.
  • the second preset threshold may be set to a reasonable value according to the actual situation.
  • the number of correct feature point pairs after matching is determined, and it is determined whether it is greater than the first preset threshold. If the value is smaller than the first preset threshold, the feature matching is performed according to the preset feature matching algorithm, and the matching result is filtered again. According to this process, the calculation is iteratively continued until the number of matching feature point pairs is greater than the first predetermined threshold.
  • the number of iteration calculations may be set in advance. In the actual calculation process, the number of iteration calculations is counted. When the number of iterations reaches a preset number of times, the iteration is stopped, and the matching of the complete feature point pairs is performed. And screening.
  • A3. Calculate corresponding three-dimensional coordinates of the matching feature point pair according to the two-dimensional coordinates and the camera parameter, and construct three-dimensional point cloud data of the user face according to the calculated three-dimensional coordinates.
  • A4. Convert the three-dimensional point cloud data into a depth image, and use any one of the plurality of facial images as a color image of the user's face.
  • the coordinates of the feature point pairs on the two face images are obtained.
  • the spatial transformation matrix between the cameras is calculated according to the camera parameters; the corresponding three-dimensional coordinates of the correct feature point pairs are calculated according to the spatial transformation matrix and the two-dimensional coordinates.
  • the coordinate system of the image captured by the left camera is defined as O l -X l Y l , and the effective focal length of the left camera is f L; defined for the right camera coordinate system O r -x r y r z r , then the right camera coordinate system is defined as the effective focal length of the image O r -X r Y r, left camera is f r.
  • the projection model of the camera the following relationship can be obtained:
  • t x , t y , and t z are the amounts of translation of the second camera in three directions relative to the first camera, respectively.
  • ⁇ , ⁇ , ⁇ , t x , t y , t z represent the spatial relationship between the two cameras.
  • the coordinates of the correctly matched feature points calculated in the above step A2 are (X 1 , Y 1 ), (Xr, Y r ) on the two images, and the camera focal lengths f l and f r are known numbers.
  • the positional relationship between the cameras can calculate the space conversion matrix M. Therefore, the values of x and y can be calculated by the above relational expression 1, and the value of z can be calculated according to the above relational expression 4. That is, the coordinates (x, y, z) of the three-dimensional space points corresponding to (X 1 , Y 1 ), (X r , Y r ) are obtained.
  • the three-dimensional coordinates corresponding to each matched feature point are calculated, and the spatial points corresponding to the three-dimensional coordinates constitute a three-dimensional point cloud forming a face.
  • the obtained three-dimensional point cloud is converted into a depth image, and any one of the corresponding two images is used as the color image of the user.
  • steps A1 to A4 to obtain a preset number of depth images and color images of the user, and use the depth image and the color image as inputs of a preset two-channel convolutional neural network model to train the pair.
  • the channel convolutional neural network model determines model parameters
  • a two-channel convolutional neural network model that determines model parameters is used as a face recognition model, wherein the two-channel convolutional neural network model takes the results of the fully connected layer as an output.
  • Construct a two-channel convolutional neural network model which does not need to classify the output, and takes the result of the fully connected layer as an output, and outputs the result as a feature vector.
  • the input of one channel of the model is a color image
  • the input of the other channel is a depth image.
  • the depth image and the color image of the plurality of users are obtained as sample data, and all the sample data are divided into training samples and test samples according to a preset ratio, and the above model is trained and verified to obtain model parameters.
  • a two-channel convolutional neural network model with model parameters is determined as a face recognition model.
  • the face recognition model is applied to the face recognition process as follows:
  • Face registration process acquiring a multi-view face image of a user to be registered, acquiring a depth image and a color image of a face of the user to be registered according to the multi-view face image, and inputting the depth image and the color image into the trained face recognition In the model, a feature vector corresponding to the face image of the user is obtained.
  • the face recognition process acquiring a multi-view face image of the user to be recognized, acquiring a depth image and a color image of the face of the user to be registered according to the multi-view face image, and inputting the acquired depth image and color image to the trained person
  • a feature vector corresponding to the face image of the user to be identified is obtained; and an Euclidean distance between the feature vector of the user to be identified and the feature vector of the registered user is calculated, and if the calculated Euclidean distance is less than a preset threshold, It is determined that the user to be identified is the same person as the registered user, otherwise, it is determined that the user to be identified is not the same person as the registered user.
  • three or more cameras may be set to collect facial images of users with more viewing angles.
  • pairwise matching is performed to obtain multiple sets of three-dimensional point cloud data, and multiple sets of three-dimensional point cloud data are merged into a complete set of point cloud data.
  • the device for generating a face recognition model collects a plurality of face images of a user photographed from a plurality of angles of view, and acquires camera parameters for capturing a plurality of face images, according to a preset feature matching algorithm.
  • the acquired facial images are matched by two pairs, and the matched feature point pairs are obtained, and the matched feature point pairs are filtered by a preset feature point screening algorithm to delete the matching feature point pairs, and the correct feature point pairs are retained.
  • the three-dimensional coordinates are used to construct the three-dimensional point cloud data of the user's face according to the calculated three-dimensional coordinates, and the three-dimensional point cloud data is converted into a depth image to construct a two-channel convolutional neural network model, and the two channels are respectively used to input the depth image and a color image into which the depth image and color image of a plurality of users collected according to the above process are input to the two-channel convolutional neural network In the model, the model parameters are obtained.
  • the depth image is used as the input feature of the model, which reflects the three-dimensional characteristics of the facial features. Compared with the recognition of the traditional two-dimensional facial features, this recognition method is not easily accepted by the gesture. The influence of non-geometric appearance changes such as expressions and illumination improves the accuracy of face recognition.
  • the present application also provides a method for generating a face recognition model.
  • FIG. 2 it is a flowchart of a preferred embodiment of a method for generating a face recognition model of the present applicant. The method can be performed by a device that can be implemented by software and/or hardware.
  • the method for generating a face recognition model includes:
  • Step S10 collecting a plurality of face images of the user photographed from the plurality of angles of view, and acquiring camera parameters for capturing the plurality of face images.
  • Step S20 Matching the acquired plurality of facial images according to a preset feature matching algorithm to obtain matching feature point pairs, and screening the matched feature point pairs by a preset feature point screening algorithm to delete Match the wrong feature point pairs and obtain the 2D coordinates of the matching feature point pair on the face image.
  • two cameras set at different viewing angles are set to capture the facial image of the user, and two facial images of different angles are obtained.
  • the relative positional relationship between the two cameras and the effective focal length of the camera are known.
  • Feature point matching is performed on two facial images according to a preset feature matching algorithm.
  • the preset feature matching algorithm may be an ORB (Oriented FAST and Rotated BRIEF) algorithm, and the ORB algorithm is a fast feature point extraction and description.
  • the algorithm can detect and match the feature points of the two facial images to find matching feature point pairs in the two images.
  • a SIFT (Scale-invariant feature transform) algorithm may also be used to perform feature pair calculation.
  • the number of feature point pairs matched by these feature matching algorithms is small, and there may be a certain amount of feature points matching errors. Therefore, after the feature matching is completed, the acquired feature point pairs need to be filtered to delete the feature point pairs that match the errors, and the accuracy of the face recognition is improved.
  • step S20 may include the following refinement steps:
  • the screening is stopped, and the two-dimensional coordinates of the matching feature point pair on the facial image are obtained; if not, the matching feature point pair is further obtained according to the feature matching algorithm, and the feature point filtering algorithm is obtained according to the feature point matching algorithm.
  • the matched feature point pairs are filtered until the number of matching feature point pairs is greater than the first preset threshold.
  • the preset feature point screening algorithm is specifically as follows: the acquired facial image is divided into K ⁇ K grid regions, wherein the size of the K value may be determined according to pixels of the collected facial image, for example, a photo The size is 1600 ⁇ 1600, then the K value can be set to 80, then there are 20 ⁇ 20 pixel points in each grid area, wherein one feature point corresponds to one pixel point, and the matching in each grid area is counted.
  • the number of feature point pairs that the feature points match in their L ⁇ L neighborhood, preferably, in one embodiment, L 3, then the neighborhood of a grid area is the other 8 networks adjacent to it. Grid area. Based on the principle of motion smoothness, there must be more matching feature points in the neighborhood of the matched feature points.
  • the feature point pairs matched according to the preset feature matching algorithm are counted to determine whether the matching result is correct. If the number of statistical feature point pairs is smaller than the second preset threshold in the neighborhood of a feature point, the feature point is determined to be a pair of feature points that match the error.
  • the statistical feature point pair If a feature point is in the neighborhood of the feature point, the statistical feature point pair If the number is greater than or less than the second predetermined threshold, then the feature point is determined to be the correct feature point pair.
  • the second preset threshold may be set to a reasonable value according to actual conditions.
  • the number of correct feature point pairs after matching is determined, and it is determined whether it is greater than the first preset threshold. If the value is smaller than the first preset threshold, the feature matching is performed according to the preset feature matching algorithm, and the matching result is filtered again. According to this process, the calculation is iteratively continued until the number of matching feature point pairs is greater than the first predetermined threshold.
  • the number of iteration calculations may be set in advance. In the actual calculation process, the number of iteration calculations is counted. When the number of iterations reaches a preset number of times, the iteration is stopped, and the matching of the complete feature point pairs is performed. And screening.
  • Step S30 calculating corresponding three-dimensional coordinates of the matching feature point pair according to the two-dimensional coordinates and the camera parameter, and constructing three-dimensional point cloud data of the user face according to the calculated three-dimensional coordinates.
  • Step S40 converting the three-dimensional point cloud data into a depth image, and using any one of the plurality of facial images as a color image of the user's face.
  • the coordinates of the feature point pairs on the two face images are obtained.
  • the spatial transformation matrix between the cameras is calculated according to the camera parameters; the corresponding three-dimensional coordinates of the correct feature point pairs are calculated according to the spatial transformation matrix and the two-dimensional coordinates.
  • the coordinate system of the image captured by the left camera is defined as O l -X l Y l , and the effective focal length of the left camera is f L; defined for the right camera coordinate system O r -x r y r z r , then the right camera coordinate system is defined as the effective focal length of the image O r -X r Y r, left camera is f r.
  • the projection model of the camera the following relationship can be obtained:
  • t x , t y , and t z are the amounts of translation of the second camera in three directions relative to the first camera, respectively.
  • ⁇ , ⁇ , ⁇ , t x , t y , t z represent the spatial relationship between the two cameras.
  • the coordinates of the correctly matched feature points calculated in the above step S20 are (X 1 , Y 1 ), (X r , Y r ) on the two images, and the camera focal lengths f l and f r are known numbers.
  • the spatial transformation matrix M can be calculated by the positional relationship between the cameras, and therefore, the values of x and y can be calculated by the above relational expression 1, and the value of z can be calculated according to the above relational expression 4. That is, the coordinates (x, y, z) of the three-dimensional space points corresponding to (X 1 , Y 1 ), (X r , Y r ) are obtained.
  • the three-dimensional coordinates corresponding to each matched feature point are calculated, and the spatial points corresponding to the three-dimensional coordinates constitute a three-dimensional point cloud forming a face.
  • the obtained three-dimensional point cloud is converted into a depth image, and any one of the corresponding two images is used as the color image of the user.
  • Step S50, step S10 to step S40 are repeatedly performed to acquire a preset number of depth images and color images of the user, and the depth image and the color image are used as inputs of a preset two-channel convolutional neural network model, and the training is performed.
  • a two-channel convolutional neural network model is used to determine model parameters, and a two-channel convolutional neural network model that determines model parameters is used as a face recognition model, wherein the two-channel convolutional neural network model takes the results of the fully connected layer as an output.
  • Construct a two-channel convolutional neural network model which does not need to classify the output, and takes the result of the fully connected layer as an output, and outputs the result as a feature vector.
  • the input of one channel of the model is a color image
  • the input of the other channel is a depth image.
  • the depth image and the color image of the plurality of users are acquired as sample data, and all the sample data are divided into training samples and test samples according to a preset ratio, and the model is trained and verified to obtain model parameters.
  • a two-channel convolutional neural network model with model parameters is determined as a face recognition model.
  • the face recognition model is applied to the face recognition process as follows:
  • Face registration process acquiring a multi-view face image of a user to be registered, acquiring a depth image and a color image of a face of the user to be registered according to the multi-view face image, and inputting the depth image and the color image into the trained face recognition In the model, a feature vector corresponding to the face image of the user is obtained.
  • the face recognition process acquiring a multi-view face image of the user to be recognized, acquiring a depth image and a color image of the face of the user to be registered according to the multi-view face image, and inputting the acquired depth image and color image to the trained person
  • a feature vector corresponding to the face image of the user to be identified is obtained; and an Euclidean distance between the feature vector of the user to be identified and the feature vector of the registered user is calculated, and if the calculated Euclidean distance is less than a preset threshold, It is determined that the user to be identified is the same person as the registered user, otherwise, it is determined that the user to be identified is not the same person as the registered user.
  • three or more cameras may be set to collect facial images of users with more viewing angles.
  • pairwise matching is performed to obtain multiple sets of three-dimensional point cloud data, and multiple sets of three-dimensional point cloud data are merged into a complete set of point cloud data.
  • a method for generating a face recognition model collecting a plurality of face images of a user photographed from a plurality of angles of view, and acquiring camera parameters for capturing a plurality of face images, according to a preset feature matching algorithm
  • the acquired facial images are matched by two pairs, and the matched feature point pairs are obtained, and the matched feature point pairs are filtered by a preset feature point screening algorithm to delete the matching feature point pairs, and the correct feature point pairs are retained.
  • the three-dimensional coordinates are used to construct the three-dimensional point cloud data of the user's face according to the calculated three-dimensional coordinates, and the three-dimensional point cloud data is converted into a depth image to construct a two-channel convolutional neural network model, and the two channels are respectively used to input the depth image and a color image into which the depth image and color image of a plurality of users collected according to the above process are input to the two-channel convolutional neural network In the model, the model parameters are obtained.
  • the depth image is used as the input feature of the model, which reflects the three-dimensional characteristics of the facial features. Compared with the recognition of the traditional two-dimensional facial features, this recognition method is not easily accepted by the gesture. The influence of non-geometric appearance changes such as expressions and illumination improves the accuracy of face recognition.
  • the embodiment of the present application further provides a computer readable storage medium, where the model generation program 01 is stored, and the model generation program 01 can be executed by one or more processors to implement the following operating:
  • the specific embodiment of the computer readable storage medium of the present application is substantially the same as the foregoing embodiments of the apparatus and method for generating a face recognition model, and is not described herein.
  • B2 Matching the acquired plurality of facial images according to a preset feature matching algorithm to obtain matching feature point pairs, and filtering the matched feature point pairs by a preset feature point screening algorithm to delete the matching Wrong feature point pairs and obtain two-dimensional coordinates matching the correct feature point pairs on the face image;
  • step B1 to step B4 are repeatedly performed to obtain a preset number of depth images and color images of the user, and the depth image and the color image are used as inputs of a preset two-channel convolutional neural network model, and the pair is trained.
  • the channel convolutional neural network model determines model parameters
  • a two-channel convolutional neural network model that determines model parameters is used as a face recognition model, wherein the two-channel convolutional neural network model takes the results of the fully connected layer as an output.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

L'invention concerne un dispositif de génération d'un modèle de reconnaissance faciale, comprenant une mémoire et un processeur. La mémoire enregistre un programme de génération de modèle pouvant être exécuté sur le processeur. Lorsque le programme est exécuté par le processeur, les étapes suivantes sont mises en œuvre : acquisition de plusieurs images de visage d'un utilisateur capturées selon plusieurs angles de vue, et obtention de paramètres de caméra; mise en correspondance desdites images de visage par paires pour obtenir des paires de points caractéristiques appariés, et criblage des paires de points caractéristiques pour obtenir des coordonnées bidimensionnelles de paires de points caractéristiques correctement appariés; calcul des coordonnées tridimensionnelles des paires de points caractéristiques, et construction de données en nuage de points tridimensionnelles du visage de l'utilisateur; conversion des données de nuage de points tridimensionnelles en images de profondeur; et obtention d'un nombre prédéfini d'images de profondeur et d'images couleur de l'utilisateur en tant qu'entrée d'un modèle de réseau neuronal convolutionnel à double canal, et apprentissage du modèle pour déterminer des paramètres de modèle. La présente invention concerne également un procédé de génération d'un modèle de reconnaissance faciale, et un support d'informations lisible par ordinateur. La présente invention permet d'améliorer la précision d'une reconnaissance faciale.
PCT/CN2018/102401 2018-04-09 2018-08-27 Dispositif et procédé de génération de modèle de reconnaissance faciale, et support d'informations lisible par ordinateur WO2019196308A1 (fr)

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