WO2019192121A1 - Procédé d'apprentissage de modèle de réseau neuronal à double canal et de comparaison de visage humain, ainsi que terminal et support - Google Patents

Procédé d'apprentissage de modèle de réseau neuronal à double canal et de comparaison de visage humain, ainsi que terminal et support Download PDF

Info

Publication number
WO2019192121A1
WO2019192121A1 PCT/CN2018/100152 CN2018100152W WO2019192121A1 WO 2019192121 A1 WO2019192121 A1 WO 2019192121A1 CN 2018100152 W CN2018100152 W CN 2018100152W WO 2019192121 A1 WO2019192121 A1 WO 2019192121A1
Authority
WO
WIPO (PCT)
Prior art keywords
face
feature
face image
neural network
normalized
Prior art date
Application number
PCT/CN2018/100152
Other languages
English (en)
Chinese (zh)
Inventor
王义文
王健宗
肖京
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2019192121A1 publication Critical patent/WO2019192121A1/fr

Links

Images

Classifications

    • 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
    • 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 image recognition technologies, and in particular, to a two-channel neural network model training and a face comparison method, a terminal, and a medium.
  • biometrics have been widely used in identity authentication because of their portability, loss, no forgetting, no borrowing, and no misappropriation.
  • face recognition technology is the most direct, friendly and convenient, and is our ideal choice.
  • Face matching is a sub-area of face recognition. Face comparison is to judge whether two faces are the same person. The most common scene is to judge whether the certificate is the person or not. Face recognition is given a face. The picture, and then judge who this person is, its essence is equivalent to multiple face comparisons.
  • the face image in the dynamic environment has many effects such as insufficient illumination, occlusion, insufficient resolution, and incorrect posture, the face matching in the dynamic environment is very difficult, resulting in a decrease in the accuracy of face comparison.
  • a first aspect of the present application provides a two-channel neural network model training method, the method comprising:
  • each original face image has a size of 182*182;
  • the dual channel neural network model training End and update the weights and offsets in the two-channel neural network model.
  • a second aspect of the present application provides a method for face alignment using the two-channel neural network model, the method comprising:
  • the histogram of the target user After receiving the face image of the target user to be compared, the histogram of the target user is subjected to histogram equalization processing to obtain a histogram equalized face image of the target user;
  • the face comparison result is determined according to the relationship between the calculated similarity and the preset confidence threshold.
  • a third aspect of the present application provides a terminal, the terminal comprising a processor, the processor is configured to implement the dual channel neural network model training method or implement the face ratio when executing computer readable instructions stored in a memory The method.
  • a fourth aspect of the present application provides a non-volatile readable storage medium having stored thereon computer readable instructions, the computer readable instructions being implemented by a processor to implement the The two-channel neural network model training method or the face matching method is implemented.
  • the two-channel neural network model training method, the face comparison method, the terminal and the medium described in the present application apply the trained two-channel neural network model to the face comparison, and can solve the existence of the face image in the dynamic environment. Insufficient illumination, occlusion, insufficient resolution, incorrect posture, etc.; only a small number of samples are needed in the training phase of the two-channel neural network model, thus solving the problem of the data volume requirement of the algorithm and increasing the practicability of the algorithm. Improve the accuracy and efficiency of witness verification.
  • FIG. 1 is a flowchart of a two-channel neural network model training method according to Embodiment 1 of the present application.
  • Embodiment 2 is a flow chart of a two-channel neural network model training method provided by Embodiment 2 of the present application.
  • FIG. 3 is a flowchart of a face matching method according to Embodiment 3 of the present application.
  • Embodiment 4 is a flowchart of a face matching method provided in Embodiment 4 of the present application.
  • FIG. 5 is a structural diagram of a two-channel neural network model training apparatus according to Embodiment 5 of the present application.
  • FIG. 6 is a structural diagram of a face matching device according to Embodiment 6 of the present application.
  • FIG. 7 is a schematic diagram of a terminal provided in Embodiment 7 of the present application.
  • the face matching method of the present application is applied to one or more terminals.
  • the two-channel neural network model training method and the face comparison method can also be applied to a hardware environment composed of a terminal and a server connected to the terminal through a network.
  • the two-channel neural network model training method and the face comparison method of the embodiment of the present application may be performed by the server at the same time, or may be performed by the terminal at the same time; or may be performed by the server and the terminal together, for example, the dual channel nerve
  • the network model training method is performed by a server, and the face matching method is executed by a terminal, or the face matching method is executed by a server, and the two-channel neural network model training method is executed by a terminal.
  • This application is not limited herein.
  • FIG. 1 is a flowchart of a two-channel neural network model training method according to Embodiment 1 of the present application.
  • the obtaining of the original face image may include the following two methods:
  • the resolution of the photographing device for example, camera, camera, etc.
  • the face of different people can be photographed in a dynamic environment to obtain the original face image of size 182*182;
  • the LFW data set was created to investigate the face recognition problem in an unrestricted environment, which contains more than 13,000 face images, all of which are from the Internet, not the lab environment.
  • each original face image is randomly cut into five first face images of the same size, and each first face image is Both contain face areas.
  • Randomly crop each original face image to obtain the first face image under arbitrary angle and arbitrary illumination conditions is the process of collecting the original face image in the simulated dynamic environment; secondly, by cutting In this way, multiple first face images can be obtained, thereby increasing the number of face sample data sets, and more face sample data sets can improve the robustness of the two-channel neural network model.
  • the specific steps of performing histogram equalization processing on the first face image in the face sample data set include:
  • n is the sum of the number of pixels in the first face picture
  • n k is the number of pixels of the gray level r k
  • L is the total number of gray levels in the first face picture
  • the calculated pixel grayscale distribution density is transformed according to formula (1-2).
  • the gray value is mapped to obtain a new pixel gray level, and the new pixel gray level is rounded to obtain a transformed new pixel gray level.
  • Performing a histogram equalization process on each of the first face images in the face sample data set which can further enhance the contrast of the first face image in the face sample data set, especially for exposure
  • the first face image that is excessive or underexposed can better maintain the detail information of the face region in the first face image after the histogram equalization processing.
  • the color picture needs to be The color space is converted into an HSV (Hue Chroma, Value Saturation) color space from the RGB color space, and then the histogram equalization process is performed on the V component.
  • HSV Human Chroma, Value Saturation
  • the first deep neural network and the second deep neural network are neural networks having the same number of layers (for example, 200 layers).
  • the first depth neural network and the second deep neural network adopt a perceptron-based Inception-Resnet-V2 (Inception-V2 and Resnet combined neural network) neural network.
  • the perceptron-based Inception-Resnet-V2 neural network is a prior art, and the present application does not repeat here.
  • the step 104 may further include: the dimension of the first facial feature extracted by the first depth neural network is 64 dimensions, and the second person extracted by the second deep neural network The dimension of the face feature is 64 dimensions.
  • the first facial feature and the second facial feature may be normalized by a normalization function, and the normalization function may be a Euclidean distance function or a Manhattan Distance function, minimum absolute error.
  • the first normalized face feature is obtained by using the Euclidean distance function on the first facial feature, and the second facial feature is normalized to obtain a second normalized facial feature.
  • Normalization can compress light, shadows, and edges so that the first normalized face feature and the second normalized face feature are robust to illumination, shadow, and edge changes, further enhancing the two-channel neural network. Model robustness.
  • normalization using the Euclidean distance function can avoid over-fitting of the two-channel neural network model, thereby improving the generalization ability of the two-channel neural network model, and optimizing the weight and offset of the subsequent two-channel neural network model. Become more stable and fast.
  • the preset connection rule may be a cross connection.
  • the first normalized face feature is (x 1 , x 2 , x 3 , . . . , x m )
  • the second normalized face feature is (y 1 , y 2 , y 3 , . . . , y m )
  • the final face features obtained by the cross-connection connection are expressed as (x 1 , y 1 , x 2 , y 2 , x 3 , y 3 , . . . , x m , y m ).
  • the preset connection rules may be sequential connections.
  • the first normalized face feature is (x 1 , x 2 , x 3 , . . . , x m )
  • the second normalized face feature is (y 1 , y 2 , y 3 , . . . , y m )
  • the final face features obtained by sequential connection are expressed as (x 1 , x 2 , x 3 , . . . , x m , y 1 , y 2 , y 3 , . . . , y m ).
  • 107 normalize the final facial feature representation and input to a preset loss function to calculate a loss function value.
  • the loss function value is less than or equal to a preset loss function threshold, the dual channel neural network model training Ending and updating the weights and offsets in the two-channel neural network model; when the loss function value is greater than a preset loss function threshold, acquiring more original face images and re-executing based on the increased original face images.
  • the preset loss function L is represented by the following formula (1-3):
  • L S represents the result of the cross-entropy loss function (softmax loss). Represents the actual output of the yi person, L C represents the result of the center loss function, cyi represents the feature center of the yi person, and xi represents the feature before the fully connected layer.
  • the preset loss function can make the distance between different face images larger, and the distance between different face images of the same person is reduced, thereby improving the classification of the two-channel neural network model.
  • the dual-channel neural network model trained in the present application can significantly enhance the feature extraction capability of the face image in the input face sample data set and the face image processed by the histogram equalization process. It can reduce the risk of performance degradation when the network level is deepened, and improve the robustness of the two-channel neural network model, which is convenient for improving the recognition rate of face recognition or face comparison.
  • Embodiment 2 is a flow chart of a two-channel neural network model training method provided by Embodiment 2 of the present application.
  • the step 201 in this embodiment is the same as the step 101 in the first embodiment, and the application is not described in detail herein.
  • the second face image size obtained after random cropping is 171*160, which not only can maintain the facial detail information in the original face image to the greatest extent, but also utilizes the original face image.
  • the training time of the two-channel neural network model can be shortened, and the training speed of the two-channel neural network model can be improved.
  • the pre-stored face detection algorithm may be one of the following algorithms: a template-based face detection method, an artificial neural network-based face detection method, a model-based face detection method, and a skin color-based face detection method. The method or the face detection method based on the feature sub-face, and the like.
  • the pre-stored face detection algorithm is not limited to the above enumeration, and any algorithm capable of detecting whether the face image is included in the cropped face image may be referred to herein.
  • the face detection algorithm pre-stored in this embodiment is a prior art, and will not be described in detail herein.
  • each of the second face images in the cropped second face image includes a face region
  • performing the following step 204 when determining each of the cropped second face images or When the face area is not included in the partial face picture, the following step 205 is performed.
  • the face region that is not included in the cropped second face image includes the following two situations: there is no face region at all; only a part of the face region exists.
  • the step 204 in this embodiment is the same as the step 103 in the first embodiment, and the application is not described in detail herein.
  • the step 206 in this embodiment is the same as the step 104 in the first embodiment, and the application is not described in detail herein.
  • the step 207 in this embodiment is the same as the step 105 in the first embodiment, and the application is not described in detail herein.
  • the step 208 in this embodiment is the same as the step 106 in the first embodiment, and the application is not described in detail herein.
  • the step 209 in this embodiment is the same as the step 107 in the first embodiment, and the present application will not be described in detail herein.
  • the two-channel neural network model trained in the present application detects each of the cropped second face images by a pre-stored face detection algorithm to ensure that each second face image is included. All the face regions can ensure the validity of the extracted face features, which can further improve the robustness of the two-channel neural network model, and facilitate the subsequent recognition of face recognition or face comparison.
  • FIG. 3 is a flowchart of a face matching method according to Embodiment 3 of the present application.
  • the feature vector A represents the final feature representation of the target user
  • the feature vector B represents the final feature representation of the registered user.
  • the cosine value between the feature vector A and each of the feature vectors B in the database is calculated according to the equation (2-1), and the range of the calculated cosine value is [-1, 1].
  • the SIM calculated according to equation (2-2) is the similarity, and the value of the similarity is normalized to [0, 1].
  • the results of the face alignment include alignment results and comparison failure results.
  • the calculated similarity is greater than or equal to the preset confidence threshold, it is determined that the user matches, that is, the registered user who is the same person as the target user is matched in the database.
  • the calculated similarity is less than the preset confidence threshold, it is determined that the user comparison fails, that is, the registered user who is the same person as the target user is not matched in the database.
  • the preset confidence threshold is 0.5.
  • the dual-channel neural network model is used for face comparison, which can improve the accuracy of face comparison, shorten the time of face comparison, and improve the efficiency of face comparison.
  • Embodiment 4 is a flowchart of a face matching method provided in Embodiment 4 of the present application.
  • the pre-processing may include, but is not limited to, image denoising, illumination normalization, pose calibration, grayscale normalization, and the like.
  • the face image of the target user After receiving the face image of the target user to be compared, the face image of the target user is pre-processed to further improve the face image of the target user, so as to extract more discriminative power. Face features.
  • 403 Input the pre-processed target user's face image into the first deep neural network in the trained two-channel neural network model to extract the third facial feature, and simultaneously equalize the target user's histogram.
  • the fourth face feature is extracted from the second deep neural network in the trained two-channel neural network model.
  • the pre-set sorting method includes, but is not limited to, sorting from large to small, and sorting from small to large.
  • the results of the face alignment include alignment results and comparison failure results.
  • the maximum similarity is greater than or equal to the preset confidence threshold, it is determined that the user matches, that is, the registered user who is the same person as the target user is matched in the database.
  • the maximum similarity is less than the preset confidence threshold, it is determined that the user comparison fails, that is, the registered user who is the same person as the target user is not matched in the database.
  • the preset confidence threshold is 0.5.
  • the user comparison is determined. by.
  • the maximum similarity is less than the preset confidence threshold, it is determined that the user comparison fails. This can shorten the comparison time, and only need to compare once to determine whether the target user is a registered user.
  • the face matching method described in the present application can extract a better facial feature representation by using a two-channel neural network model, and a better face can be obtained by taking a shorter face comparison time. Compare effects.
  • FIG. 5 is a functional block diagram of a preferred embodiment of the dual channel neural network model training device of the present application.
  • the two-channel neural network model training device 50 operates in the terminal 7.
  • the dual channel neural network model training device 50 can include a plurality of functional modules comprised of computer readable instruction code segments. Each of the computer readable instruction code segments in the dual channel neural network model training device 50 may be stored in the memory 71 and executed by the at least one processor 72 for execution (see Figures 1 and 2 for details). Description) Training for a two-channel neural network model.
  • the dual channel neural network model training device 50 can be divided into multiple functional modules according to the functions performed by the dual channel neural network model.
  • the function module may include: an obtaining module 501, a cropping module 502, a first histogram processing module 503, a first feature extraction module 504, a first normalization module 505, a first feature connection module 506, and a model update module 507. And a face detection module 508.
  • the obtaining module 501 is configured to obtain a raw face image of different people, and the size of each original face image is 182*182.
  • the cropping module 502 is configured to randomly cut each original face image into a preset number of first face images to obtain a face sample data set.
  • the preset number can be adjusted or modified according to actual needs.
  • the cropping module 502 randomly cuts each original face image into five first face images of the same size, each of which The first face image contains a face area.
  • Randomly crop each original face image to obtain the first face image under arbitrary angle and arbitrary illumination conditions is the process of collecting the original face image in the simulated dynamic environment; secondly, by cutting In this way, multiple first face images can be obtained, thereby increasing the number of face sample data sets, and more face sample data sets can improve the robustness of the two-channel neural network model.
  • the histogram processing module 503 is configured to perform histogram equalization processing on each first face image in the face sample data set to obtain a histogram equalized face image.
  • the first feature extraction module 504 is configured to input each first face image in the face sample data into the first depth neural network to extract the first facial feature, and simultaneously equalize the corresponding histogram.
  • the picture input second depth neural network extracts the second face feature.
  • the first deep neural network and the second deep neural network are neural networks having the same number of layers (for example, 200 layers).
  • the first depth neural network and the second deep neural network adopt a perceptron-based Inception-Resnet-V2 (Inception-V2 and Resnet combined neural network) neural network.
  • the perceptron-based Inception-Resnet-V2 neural network is a prior art, and the present application does not repeat here.
  • the first feature extraction module 504 is further configured to: the dimension of the first facial feature extracted by the first depth neural network is 64 dimensions, and the second depth neural network extracts The dimension of the second face feature is 64 dimensions.
  • a first normalization module 505 configured to normalize the first facial feature to obtain a first normalized facial feature, and normalize the second facial feature to obtain a second normalized Face features.
  • a first feature connection module 506 configured to connect the first normalized face feature and the second normalized face feature according to a preset connection rule to obtain a new normalized face feature, as a final face Feature representation.
  • the model update module 507 is configured to normalize the final facial feature representation and input the value to the preset loss function to calculate a loss function value.
  • the loss function value is less than or equal to a preset loss function threshold
  • the double The channel neural network model training ends and updates the weights and offsets in the two-channel neural network model; when the loss function value is greater than the preset loss function threshold, more original face images are acquired, and based on the increased original The face picture re-executes the above modules 501-507.
  • the cropping module 502 is further configured to randomly cut each original face image into a preset number of second face images to obtain a face sample data set, where each of the face sample data sets The size of the second face image is 171*160.
  • the second face image size obtained after random cropping is 171*160, which not only can maintain the facial detail information in the original face image to the greatest extent, but also utilizes the original face image.
  • the training time of the two-channel neural network model can be shortened, and the training speed of the two-channel neural network model can be improved.
  • the dual-channel neural network model training device 50 further includes: a face detection module 508, configured to detect each of the cropped second face images by using a pre-stored face detection algorithm, and determine the cropping Whether or not the face area is included in each second face picture in all the second face pictures.
  • a face detection module 508 configured to detect each of the cropped second face images by using a pre-stored face detection algorithm, and determine the cropping Whether or not the face area is included in each second face picture in all the second face pictures.
  • the pre-stored face detection algorithm may be one of the following algorithms: a template-based face detection method, an artificial neural network-based face detection method, a model-based face detection method, and a skin color-based face detection method. The method or the face detection method based on the feature sub-face, and the like.
  • the pre-stored face detection algorithm is not limited to the above enumeration, and any algorithm capable of detecting whether the face image is included in the cropped face image may be referred to herein.
  • the face detection algorithm pre-stored in this embodiment is a prior art, and will not be described in detail herein.
  • the first histogram processing module 503 is further configured to: when the face detection module 508 determines that each of the second face images in the cropped second face image includes a face region, Histogram equalization processing is performed on each second face image in the face sample data set to obtain a histogram equalization face image.
  • the cropping module 502 is further configured to: when the face detection module 508 determines that each of the second face images that are cropped includes no face region in the face image, the pair does not include The original face image corresponding to the second face image of the face region is randomly cropped until the preset number of second face images are obtained.
  • the face region that is not included in the cropped second face image includes the following two situations: there is no face region at all; only a part of the face region exists.
  • the first normalization module 505 is further configured to input each second face image in the face sample data into the first depth neural network to extract the first facial feature, and correspondingly The histogram equalization face image input second depth neural network extracts the second face feature.
  • the dual-channel neural network model trained in the present application can significantly enhance the feature extraction capability of the face image in the input face sample data set and the face image processed by the histogram equalization process. It mitigates the risk of performance degradation when the network level is deepened, and improves the robustness of the two-channel neural network model.
  • each second face image that is cropped is detected by a pre-stored face detection algorithm to ensure each piece.
  • the second face image contains all the face regions, which can ensure the validity of the extracted face features, and can further improve the robustness of the two-channel neural network model, and facilitate the subsequent improvement of face recognition or face comparison. Recognition rate.
  • FIG. 6 is a functional block diagram of a preferred embodiment of the applicant's face matching device.
  • the face matching device 60 operates in the terminal 7.
  • the face matching device 60 can include a plurality of functional modules comprised of computer readable instruction code segments.
  • the instruction codes of the respective computer readable instruction segments in the face matching device 60 may be stored in the memory 71 and executed by the at least one processor 72 for execution (see Figures 3 and 4 for details). Description) The comparison of faces.
  • the face matching device 60 of the terminal 7 can be divided into a plurality of functional modules according to the functions performed by the terminal 7.
  • the function module may include: a second histogram processing module 601, a second feature extraction module 602, a second normalization module 603, a second feature connection module 604, a calculation module 605, a result determination module 606, and a pre-processing module 607. And sorting module 608.
  • the second histogram processing module 601 is configured to perform histogram equalization processing on the face image of the target user after receiving the face image of the target user to be compared by the face, and obtain a histogram equalization of the target user. Face picture.
  • a second feature extraction module 602 configured to input a face image of the target user into a first depth neural network in the trained two-channel neural network model, and extract a third facial feature, and simultaneously The fourth facial feature is extracted from the second deep neural network in the two-channel neural network model trained by the histogram equalization face image input;
  • the second normalization module 603 is configured to normalize the third facial feature to obtain a third normalized facial feature, and normalize the fourth facial feature to obtain a fourth normalized facial feature. ;
  • the second feature connection module 604 is configured to connect the third normalized face feature and the fourth normalized face feature according to the preset connection rule to obtain a new normalized face feature. The final feature representation of the target user.
  • the calculating module 605 is configured to calculate a similarity between the final feature representation of the target user and the final feature representation of the registered user in the database.
  • the result determining module 606 is configured to determine a face comparison result according to a relationship between the calculated similarity and a preset confidence threshold.
  • the results of the face alignment include alignment results and comparison failure results.
  • the result determination module 606 determines that the user matches, that is, the registered user who is the same person as the target user is matched in the database.
  • the result determination module 606 determines that the user comparison fails, that is, the registered user who is the same person as the target user is not matched in the database.
  • the preset confidence threshold is 0.5.
  • the face matching device 60 further includes: a pre-processing module 607, configured to pre-process the face image of the target user after receiving the face image of the target user to be compared by the face deal with.
  • the pre-processing may include, but is not limited to, image denoising, illumination normalization, pose calibration, grayscale normalization, and the like.
  • the face image of the target user After receiving the face image of the target user to be compared, the face image of the target user is pre-processed to further improve the face image of the target user, so as to extract more discriminative power. Face features.
  • the second histogram processing module 601 further performs histogram equalization processing on the pre-processed target user's face image to obtain a histogram equalized face image of the target user.
  • the second feature extraction module 602 is further configured to input the face image of the pre-processed target user into the first deep neural network in the trained two-channel neural network model to extract the third facial feature. And extracting the fourth facial feature from the second deep neural network in the trained two-channel neural network model by inputting the histogram equalized face image of the target user;
  • the face matching device 60 further includes: a sorting module 608, configured to sort the calculated similarities according to a preset sorting method.
  • the pre-set sorting method includes, but is not limited to, sorting from large to small, and sorting from small to large.
  • the result determining module 606 is further configured to compare a relationship between the maximum similarity and a preset confidence threshold to determine a face comparison result.
  • the results of the face alignment include alignment results and comparison failure results.
  • the result determination module 606 determines that the user has passed the comparison, ie, the registered user who is the same person as the target user is matched in the database.
  • the result determination module 606 determines that the user comparison failed, ie, the registered user who is the same person as the target user is not matched in the database.
  • the face matching method described in the present application can extract a better facial feature representation by using a two-channel neural network model, and a better face can be obtained by taking a shorter face comparison time. Compare effects.
  • FIG. 7 it is a hardware structure diagram of a terminal for implementing the dual channel neural network model training method and/or the face matching method described in the present application.
  • the terminal 7 includes a memory 71, at least one processor 72, and at least one communication bus 73.
  • the structure of the terminal 7 shown in FIG. 7 does not constitute a limitation of the embodiment of the present application, and may be a bus type structure or a star structure, and the terminal 7 may further include a ratio More or less other hardware or software, or different component arrangements.
  • the memory 71 is configured to store computer readable instruction code and various data, such as the dual channel neural network model training device 50 and the face matching device 60 installed in the terminal 7, and The high speed, automatic completion of computer readable instructions or data access during the operation of the terminal 7.
  • the at least one processor 72 may be composed of an integrated circuit, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits of the same function or different functions, including one. Or a combination of a plurality of central processing units, microprocessors, digital processing chips, graphics processors, and various control chips.
  • the at least one processor 72 is a control core of the terminal 7, connecting various components of the entire terminal 7 using various interfaces and lines, by running or executing computer readable instructions or modules stored in the memory 71, and The data stored in the memory 71 is invoked to perform various functions and processing data of the terminal 7, such as executing the two-channel neural network model training device 50 and/or the face matching device 60.
  • the at least one communication bus 73 is arranged to effect connection communication between the memory 71, the at least one processor 72, and the like.
  • the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of hardware plus software function modules.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Multimedia (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Human Computer Interaction (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé d'apprentissage de modèle de réseau neuronal à double canal qui comprend les étapes consistant à : entrer chaque image faciale humaine dans des données d'échantillon de visage humain dans un premier réseau neuronal profond pour extraire une première caractéristique faciale humaine, et entrer simultanément l'image faciale humaine correspondante ayant été traitée par égalisation d'histogramme dans un second modèle de réseau neuronal profond pour extraire une seconde caractéristique faciale humaine ; normaliser la première caractéristique faciale humaine et la seconde caractéristique faciale humaine et les connecter pour servir de représentation de caractéristique faciale humaine finale ; et normaliser la représentation de caractéristique faciale humaine finale et entrer celle-ci dans une fonction de perte prédéfinie pour calculer une valeur de fonction de perte, et lorsque la valeur de fonction de perte est inférieure ou égale à un seuil de fonction de perte prédéfini, alors mettre fin à l'apprentissage du modèle de réseau neuronal à double canal et mettre à jour un poids et un biais dans le modèle de réseau neuronal à double canal. L'invention concerne également un procédé de comparaison de visage humain, un terminal et un support. Au moyen du procédé, un modèle de réseau neuronal à double canal applicable à la comparaison de visage humain peut être entraîné, et un meilleur résultat de comparaison de visage humain peut être obtenu.
PCT/CN2018/100152 2018-04-04 2018-08-13 Procédé d'apprentissage de modèle de réseau neuronal à double canal et de comparaison de visage humain, ainsi que terminal et support WO2019192121A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810299180.6A CN108446666A (zh) 2018-04-04 2018-04-04 双通道神经网络模型训练及人脸比对方法、终端及介质
CN201810299180.6 2018-04-04

Publications (1)

Publication Number Publication Date
WO2019192121A1 true WO2019192121A1 (fr) 2019-10-10

Family

ID=63199349

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/100152 WO2019192121A1 (fr) 2018-04-04 2018-08-13 Procédé d'apprentissage de modèle de réseau neuronal à double canal et de comparaison de visage humain, ainsi que terminal et support

Country Status (2)

Country Link
CN (1) CN108446666A (fr)
WO (1) WO2019192121A1 (fr)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110992329A (zh) * 2019-11-28 2020-04-10 上海微创医疗器械（集团）有限公司 一种产品表面缺陷检测方法、电子设备及可读存储介质
CN111027421A (zh) * 2019-11-26 2020-04-17 西安宏规电子科技有限公司 一种基于图的直推式半监督行人再识别方法
CN111144332A (zh) * 2019-12-30 2020-05-12 深圳云天励飞技术有限公司 一种图片聚档方法、装置和电子设备
CN111144566A (zh) * 2019-12-30 2020-05-12 深圳云天励飞技术有限公司 神经网络权重参数的训练方法、特征分类方法及对应装置
CN111198963A (zh) * 2019-12-11 2020-05-26 智慧眼科技股份有限公司 基于平均特征的目标检索方法、装置及其相关设备
CN111209839A (zh) * 2019-12-31 2020-05-29 上海涛润医疗科技有限公司 人脸识别方法
CN111324589A (zh) * 2020-02-14 2020-06-23 中国平安人寿保险股份有限公司 基于相似程度值的日志发送方法、装置和计算机设备
CN111582358A (zh) * 2020-04-30 2020-08-25 上海添玑网络服务有限公司 户型识别模型的训练方法及装置、户型判重的方法及装置
CN111612785A (zh) * 2020-06-03 2020-09-01 浙江大华技术股份有限公司 人脸图片质量评估方法、装置及存储介质
CN111709890A (zh) * 2020-06-12 2020-09-25 北京小米松果电子有限公司 一种图像增强模型的训练方法、装置及存储介质
CN112883213A (zh) * 2019-11-29 2021-06-01 深圳云天励飞技术有限公司 一种图片归档方法、装置及电子设备
CN113657289A (zh) * 2021-08-19 2021-11-16 北京百度网讯科技有限公司 阈值估计模型的训练方法、装置和电子设备
CN114004779A (zh) * 2020-07-27 2022-02-01 中移物联网有限公司 一种基于深度学习的人脸质量评价方法及装置
CN114663313A (zh) * 2022-03-25 2022-06-24 清华大学 用于增强人脸斑纹特征的视觉方法、装置、设备及介质
CN115545172A (zh) * 2022-11-29 2022-12-30 支付宝(杭州)信息技术有限公司 兼顾隐私保护和公平性的图神经网络的训练方法及装置
CN117054396A (zh) * 2023-10-11 2023-11-14 天津大学 基于双路径无乘法神经网络的拉曼光谱检测方法及装置
CN117173854A (zh) * 2023-09-13 2023-12-05 西安博深安全科技股份有限公司 一种基于深度学习的煤矿明火预警方法及系统

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109271891A (zh) * 2018-08-30 2019-01-25 成都考拉悠然科技有限公司 一种动态的人脸布控方法和系统
CN109284700B (zh) * 2018-09-05 2021-03-16 武汉斗鱼网络科技有限公司 图像中多个人脸检测的方法、存储介质、设备及系统
CN109497986A (zh) * 2018-11-22 2019-03-22 杭州脉流科技有限公司 基于深度神经网络的心电图智能分析方法、装置、计算机设备和系统
CN110163169A (zh) * 2019-05-27 2019-08-23 北京达佳互联信息技术有限公司 人脸识别方法、装置、电子设备及存储介质
CN110717377B (zh) * 2019-08-26 2021-01-12 平安科技（深圳）有限公司 人脸驾驶风险预测模型训练及其预测方法及相关设备
CN111401430B (zh) * 2020-03-12 2022-04-01 四川大学 一种基于双通道深度神经网络的图像在线分类方法
CN112529040A (zh) * 2020-11-16 2021-03-19 深圳壹账通智能科技有限公司 模型生成的方法、装置、电子设备及介质
CN113487698B (zh) * 2021-07-27 2023-12-12 深圳赛安特技术服务有限公司 基于双通道神经网络模型的表单生成方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105426963A (zh) * 2015-12-01 2016-03-23 北京天诚盛业科技有限公司 用于人脸识别的卷积神经网络的训练方法、装置及应用
CN105426860A (zh) * 2015-12-01 2016-03-23 北京天诚盛业科技有限公司 人脸识别的方法和装置
CN106682616A (zh) * 2016-12-28 2017-05-17 南京邮电大学 基于双通道特征深度学习的新生儿疼痛表情识别方法
CN107577987A (zh) * 2017-08-01 2018-01-12 广州广电卓识智能科技有限公司 身份认证的方法、系统和装置
CN107871105A (zh) * 2016-09-26 2018-04-03 北京眼神科技有限公司 一种人脸认证方法和装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9405960B2 (en) * 2014-06-17 2016-08-02 Beijing Kuangshi Technology Co., Ltd. Face hallucination using convolutional neural networks
WO2017214968A1 (fr) * 2016-06-17 2017-12-21 Nokia Technologies Oy Procédé et appareil destinés à des réseaux neuronaux convolutifs
CN106778684A (zh) * 2017-01-12 2017-05-31 易视腾科技股份有限公司 深度神经网络训练方法及人脸识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105426963A (zh) * 2015-12-01 2016-03-23 北京天诚盛业科技有限公司 用于人脸识别的卷积神经网络的训练方法、装置及应用
CN105426860A (zh) * 2015-12-01 2016-03-23 北京天诚盛业科技有限公司 人脸识别的方法和装置
CN107871105A (zh) * 2016-09-26 2018-04-03 北京眼神科技有限公司 一种人脸认证方法和装置
CN106682616A (zh) * 2016-12-28 2017-05-17 南京邮电大学 基于双通道特征深度学习的新生儿疼痛表情识别方法
CN107577987A (zh) * 2017-08-01 2018-01-12 广州广电卓识智能科技有限公司 身份认证的方法、系统和装置

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111027421A (zh) * 2019-11-26 2020-04-17 西安宏规电子科技有限公司 一种基于图的直推式半监督行人再识别方法
CN110992329A (zh) * 2019-11-28 2020-04-10 上海微创医疗器械（集团）有限公司 一种产品表面缺陷检测方法、电子设备及可读存储介质
CN110992329B (zh) * 2019-11-28 2023-06-30 上海微创卜算子医疗科技有限公司 一种产品表面缺陷检测方法、电子设备及可读存储介质
CN112883213B (zh) * 2019-11-29 2023-11-17 深圳云天励飞技术有限公司 一种图片归档方法、装置及电子设备
CN112883213A (zh) * 2019-11-29 2021-06-01 深圳云天励飞技术有限公司 一种图片归档方法、装置及电子设备
CN111198963A (zh) * 2019-12-11 2020-05-26 智慧眼科技股份有限公司 基于平均特征的目标检索方法、装置及其相关设备
CN111144566B (zh) * 2019-12-30 2024-03-22 深圳云天励飞技术有限公司 神经网络权重参数的训练方法、特征分类方法及对应装置
CN111144332A (zh) * 2019-12-30 2020-05-12 深圳云天励飞技术有限公司 一种图片聚档方法、装置和电子设备
CN111144566A (zh) * 2019-12-30 2020-05-12 深圳云天励飞技术有限公司 神经网络权重参数的训练方法、特征分类方法及对应装置
CN111209839A (zh) * 2019-12-31 2020-05-29 上海涛润医疗科技有限公司 人脸识别方法
CN111209839B (zh) * 2019-12-31 2023-05-23 上海涛润医疗科技有限公司 人脸识别方法
CN111324589A (zh) * 2020-02-14 2020-06-23 中国平安人寿保险股份有限公司 基于相似程度值的日志发送方法、装置和计算机设备
CN111582358B (zh) * 2020-04-30 2024-05-10 上海添玑网络服务有限公司 户型识别模型的训练方法及装置、户型判重的方法及装置
CN111582358A (zh) * 2020-04-30 2020-08-25 上海添玑网络服务有限公司 户型识别模型的训练方法及装置、户型判重的方法及装置
CN111612785A (zh) * 2020-06-03 2020-09-01 浙江大华技术股份有限公司 人脸图片质量评估方法、装置及存储介质
CN111612785B (zh) * 2020-06-03 2024-02-02 浙江大华技术股份有限公司 人脸图片质量评估方法、装置及存储介质
CN111709890B (zh) * 2020-06-12 2023-11-24 北京小米松果电子有限公司 一种图像增强模型的训练方法、装置及存储介质
CN111709890A (zh) * 2020-06-12 2020-09-25 北京小米松果电子有限公司 一种图像增强模型的训练方法、装置及存储介质
CN114004779A (zh) * 2020-07-27 2022-02-01 中移物联网有限公司 一种基于深度学习的人脸质量评价方法及装置
CN113657289B (zh) * 2021-08-19 2023-08-08 北京百度网讯科技有限公司 阈值估计模型的训练方法、装置和电子设备
CN113657289A (zh) * 2021-08-19 2021-11-16 北京百度网讯科技有限公司 阈值估计模型的训练方法、装置和电子设备
CN114663313A (zh) * 2022-03-25 2022-06-24 清华大学 用于增强人脸斑纹特征的视觉方法、装置、设备及介质
CN115545172B (zh) * 2022-11-29 2023-02-07 支付宝(杭州)信息技术有限公司 兼顾隐私保护和公平性的图神经网络的训练方法及装置
CN115545172A (zh) * 2022-11-29 2022-12-30 支付宝(杭州)信息技术有限公司 兼顾隐私保护和公平性的图神经网络的训练方法及装置
CN117173854A (zh) * 2023-09-13 2023-12-05 西安博深安全科技股份有限公司 一种基于深度学习的煤矿明火预警方法及系统
CN117173854B (zh) * 2023-09-13 2024-04-05 西安博深安全科技股份有限公司 一种基于深度学习的煤矿明火预警方法及系统
CN117054396A (zh) * 2023-10-11 2023-11-14 天津大学 基于双路径无乘法神经网络的拉曼光谱检测方法及装置
CN117054396B (zh) * 2023-10-11 2024-01-05 天津大学 基于双路径无乘法神经网络的拉曼光谱检测方法及装置

Also Published As

Publication number Publication date
CN108446666A (zh) 2018-08-24

Similar Documents

Publication Publication Date Title
WO2019192121A1 (fr) Procédé d'apprentissage de modèle de réseau neuronal à double canal et de comparaison de visage humain, ainsi que terminal et support
CN108764041B (zh) 用于下部遮挡人脸图像的人脸识别方法
WO2019011165A1 (fr) Procédé et appareil de reconnaissance faciale, dispositif électronique et support de stockage
US11263442B1 (en) Systems and methods for passive-subject liveness verification in digital media
WO2020244071A1 (fr) Procédé et appareil de reconnaissance de geste basée sur un réseau neuronal, support d'informations et dispositif
CN111191568B (zh) 翻拍图像识别方法、装置、设备及介质
WO2019071739A1 (fr) Procédé et appareil de détection de structure vivante de visage, support de stockage lisible et dispositif terminal
WO2015149534A1 (fr) Procédé et dispositif de reconnaissance faciale basés sur un motif binaire de gabor
JP7191061B2 (ja) ライブネス検査方法及び装置
CN109871845B (zh) 证件图像提取方法及终端设备
CN112232323B (zh) 人脸验证方法、装置、计算机设备和存储介质
TWI721786B (zh) 人臉校驗方法、裝置、伺服器及可讀儲存媒介
CN107944395B (zh) 一种基于神经网络验证人证合一的方法及系统
US20240013572A1 (en) Method for face detection, terminal device and non-transitory computer-readable storage medium
CN110378328B (zh) 一种证件图像处理方法及装置
US9525866B1 (en) Building a digital camera fingerprint from cropped or corrupted images
JP2022133378A (ja) 顔生体検出方法、装置、電子機器、及び記憶媒体
US20220327189A1 (en) Personalized biometric anti-spoofing protection using machine learning and enrollment data
CN113298158B (zh) 数据检测方法、装置、设备及存储介质
US20210174067A1 (en) Live facial recognition system and method
CN113642639B (zh) 活体检测方法、装置、设备和存储介质
CN111339897A (zh) 活体识别方法、装置、计算机设备和存储介质
US20240193987A1 (en) Face liveness detection method, terminal device and non-transitory computer-readable storage medium
CN113221842A (zh) 模型训练方法、图像识别方法、装置、设备及介质
JP3962517B2 (ja) 顔面検出方法及びその装置、コンピュータ可読媒体

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18913215

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18913215

Country of ref document: EP

Kind code of ref document: A1