WO2021012494A1 - Procédé et appareil de reconnaissance faciale basée sur l'apprentissage profond, et support de stockage lisible par ordinateur - Google Patents

Procédé et appareil de reconnaissance faciale basée sur l'apprentissage profond, et support de stockage lisible par ordinateur Download PDF

Info

Publication number
WO2021012494A1
WO2021012494A1 PCT/CN2019/116934 CN2019116934W WO2021012494A1 WO 2021012494 A1 WO2021012494 A1 WO 2021012494A1 CN 2019116934 W CN2019116934 W CN 2019116934W WO 2021012494 A1 WO2021012494 A1 WO 2021012494A1
Authority
WO
WIPO (PCT)
Prior art keywords
face
neural network
convolutional neural
training
picture
Prior art date
Application number
PCT/CN2019/116934
Other languages
English (en)
Chinese (zh)
Inventor
黄秋凤
李珊珊
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 WO2021012494A1 publication Critical patent/WO2021012494A1/fr

Links

Images

Classifications

    • 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
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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

Definitions

  • This application relates to the field of artificial intelligence technology, and in particular to a face recognition method, device and computer-readable storage medium based on Gabor filters and convolutional neural networks.
  • Face recognition is a kind of biometric recognition technology based on human facial feature information.
  • face recognition technology mainly uses cameras and other camera equipment to collect images or video streams containing human faces, and automatically detect faces in the images, and then perform a series of related operations on the detected faces.
  • the process of face recognition is the process of extracting and recognizing features from standard face images. Therefore, the quality of the extracted facial image features directly affects the final recognition accuracy, and the recognition model also plays a vital role in the accuracy of face recognition.
  • most of the current feature extraction is mainly based on manual feature extraction. This method is restricted by many factors, and the current recognition models are based on traditional machine learning algorithms. Therefore, in general, the face recognition effect is not ideal and the recognition accuracy is not high.
  • This application provides a face recognition method, device and computer-readable storage medium based on deep learning, the main purpose of which is to accurately identify a person from the face picture or video when a user inputs a face picture or video Face result.
  • a face recognition method based on deep learning includes:
  • a picture of a user's face is received, and the picture of the user's face is input to the convolutional neural network for face recognition, and the recognition result is output.
  • the web page includes a web page of an ORL face database, a Yale face database, an AR face database, and/or a FERET face database.
  • the extracting the face features of the original face image set according to the Gabor filter to obtain the face feature set includes:
  • a Gabor filter bank composed of several Gabor filters receives the original face image set
  • the Gabor filter bank sequentially performs a first convolution operation with pictures in the original face image set to obtain Gabor features
  • the Gabor features obtained by each first convolution operation are combined into a set to obtain the face feature set.
  • the first convolution operation is:
  • O y, u, v (x 1 , x 2 ) is the Gabor feature
  • M(x 1 , x 2 ) is the pixel value coordinates of the picture in the original face image set
  • ⁇ y, u, v (z) is the convolution function
  • z is the convolution operator
  • y, u, and v represent the three components of the picture
  • y is the brightness of the picture
  • u, v are the chromaticity of the picture.
  • the convolutional neural network includes a sixteen-layer convolutional layer, a sixteen-layer pooling layer, and a fully connected layer; and the input of the face feature vector set to a pre-built convolutional neural network Training in the network model until the loss function value in the convolutional neural network is less than the preset threshold to exit training, including:
  • the convolutional neural network After receiving the face feature vector set, the convolutional neural network inputs the face feature vector set to the sixteen-layer convolutional layer and sixteen-layer pooling layer to perform a second convolution operation and a maximum pooling Input to the fully connected layer after transformation operation;
  • the fully connected layer is combined with the activation function to calculate the training value, and the training value is input into the loss function of the model training layer, and the loss function calculates the loss value, and the magnitude of the loss value and a preset threshold is judged Relationship, until the loss value is less than the preset threshold, the convolutional neural network exits training.
  • this application also provides a face recognition device based on deep learning.
  • the device includes a memory and a processor.
  • the memory stores a person based on deep learning that can run on the processor.
  • a face recognition program when the face recognition program based on deep learning is executed by the processor, the following steps are implemented:
  • a picture of a user's face is received, and the picture of the user's face is input to the convolutional neural network for face recognition, and the recognition result is output.
  • the web page includes a web page of an ORL face database, a Yale face database, an AR face database, and/or a FERET face database.
  • the extracting the face features of the original face image set according to the Gabor filter to obtain the face feature set includes:
  • a Gabor filter bank composed of several Gabor filters receives the original face image set
  • the Gabor filter bank sequentially performs a first convolution operation with pictures in the original face image set to obtain Gabor features
  • the Gabor features obtained by each first convolution operation are combined into a set to obtain the face feature set.
  • the present application also provides a computer-readable storage medium that stores a face recognition program based on deep learning, and the face recognition program based on deep learning can be One or more processors execute to implement the steps of the face recognition method based on deep learning as described above.
  • the face recognition method, device and computer readable storage medium based on deep learning proposed in this application can use crawler technology to collect a large number of high-quality face data sets from the Internet, which is ready for subsequent face feature analysis and recognition Pre-based, and because most faces do not occupy the entire picture or video, according to the shape of the Gabor filter, the features of the face part are extracted from the entire picture or video, which not only reduces the cumbersomeness of manually extracting features, but also At the same time, sufficient preparations are made for the subsequent analysis of the facial features by the convolutional neural network, which can effectively analyze the facial features and produce accurate face recognition effects. Therefore, this application can achieve an efficient and accurate face recognition effect.
  • FIG. 1 is a schematic flowchart of a face recognition method based on deep learning provided by an embodiment of this application;
  • FIG. 2 is a Gabor feature generation diagram of a face recognition method based on deep learning provided by an embodiment of this application;
  • FIG. 3 is a schematic diagram of the internal structure of a face recognition device based on deep learning provided by an embodiment of the application;
  • FIG. 4 is a schematic diagram of modules of a face recognition program based on deep learning in a face recognition device based on deep learning provided by an embodiment of the application.
  • This application provides a face recognition method based on deep learning.
  • FIG. 1 it is a schematic flowchart of a face recognition method based on deep learning provided by an embodiment of this application.
  • the method can be executed by a device, and the device can be implemented by software and/or hardware.
  • the face recognition method based on deep learning includes:
  • the several face image databases include ORL face database, Yale face database, AR face database, and/or FERET face database, etc.
  • the Yale face database includes 15 people, including 11 photos per person, and each photo has changes in lighting conditions, changes in facial expressions, etc.
  • the FERET face database is Counterdrug Technology Transfer Program ( CTTP)
  • CTTP Counterdrug Technology Transfer Program
  • a face database collection activity of Face Recognition Technology Face Recognition Technology, FERET for short
  • the FERET face database includes a general face database and a general test standard.
  • the same face picture includes pictures of different expressions, lighting, postures and age groups.
  • this application uses the Urllib module of python to read web page data, such as reading the web page of the FERET face database, and capture the face image data in the web page of the FERET face database, and combine these The data composes the original face image set.
  • the Urllib module reads web pages such as Yale face database, AR face database, etc., and captures the face image data before placing it in the original face image set.
  • this application composes several Gabor filters into a Gabor filter bank, and after the Gabor filter bank receives the original face image set, the Gabor filter bank is in turn with those in the original face image set.
  • the picture is subjected to the first convolution operation to obtain Gabor features, and the Gabor features obtained from each first convolution operation are combined into a set to obtain the face feature set.
  • O y, u, v (x 1 , x 2 ) is the Gabor feature
  • M(x 1 , x 2 ) is the pixel value coordinates of the picture in the original face image set
  • ⁇ y, u, v (z) is the convolution function
  • z is the convolution operator
  • y, u, and v represent the three components of the picture
  • y is the brightness of the picture
  • u, v are the chromaticity of the picture.
  • the preferred embodiment of this application selects 40 Gabor filters to form a Gabor filter bank.
  • the 40 Gabor filters form a Gabor filter bank to read an image of the original face image set and compare it with the Gabor filter bank.
  • the filter bank performs the first convolution operation to obtain Gabor features, and the feature dimension of each Gabor feature is 40, and so on, the Gabor features form the face feature set.
  • the change from the original face image to the Gabor feature is shown in Figure 2.
  • the downsampling technology dimensionality reduction processing includes the first feature dimensionality reduction and the second feature dimensionality reduction.
  • the first feature dimensionality reduction is to sequentially extract Gabor features from the face feature set, and based on a sliding window with a matrix dimension of 2*2, from left to right and from top to bottom in the extracted Gabor A mean value sampling with a step length of 2 is performed on the feature, whereby the feature dimension of the extracted Gabor feature is reduced to 1/4 of the original dimension, and the feature dimension becomes 10, and the first feature dimensionality reduction is completed.
  • the feature dimension of the Gabor feature is reduced to 1/4 of the original dimension, and then an RBM model is connected to perform the second feature reduction.
  • the RBM is an energy model (Energy based model, EBM), which is derived from Evolved from the physical energy model, the RBM model receives input data and solves the probability distribution of the input data according to an energy function, and obtains output data after optimization based on the probability distribution.
  • EBM Energy model
  • the second feature reduction uses the face feature set after the first feature reduction as the input data of the RBM model.
  • the feature dimension of the output feature of the RBM model is 5.
  • the dimensionality reduction processing reduces the feature dimension of Gabor features from 40 to 5, and so on to process each Gabor feature and finally compose the output dimensionality reduction feature into a face feature vector set.
  • the pre-built convolutional neural network includes a sixteen-layer convolutional layer, a sixteen-layer pooling layer, and a fully connected layer.
  • the convolutional neural network receives the face feature vector set, Input the face feature vector set to the sixteen-layer convolutional layer and the sixteen-layer pooling layer to perform a second convolution operation and a maximum pooling operation, and then input to the fully connected layer;
  • the fully connected layer is combined with the activation function to calculate the training value, and the training value is input into the loss function of the model training layer.
  • the loss function calculates the loss value, and judges the loss value and the preset value. The size relationship of the threshold value, until the loss value is less than the preset threshold value, the convolutional neural network exits training.
  • ⁇ ' is the output data
  • ⁇ is the input data
  • k is the size of the convolution kernel
  • s is the stride of the convolution operation
  • p is the data zero-filling matrix
  • the maximum pooling operation is to select a matrix in the matrix The largest value in the data replaces the entire matrix
  • the activation function is:
  • n is the size of the original picture set
  • y t is the training value
  • ⁇ t is the original picture set
  • the preset threshold is generally set at 0.01.
  • the invention also provides a face recognition device based on deep learning.
  • FIG. 3 it is a schematic diagram of the internal structure of a face recognition device based on deep learning provided by an embodiment of this application.
  • the face recognition apparatus 1 based on deep learning may be a PC (Personal Computer, personal computer), or a terminal device such as a smart phone, a tablet computer, or a portable computer, or a server.
  • the face recognition device 1 based on deep learning at least includes 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, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc.
  • the memory 11 may be an internal storage unit of the face recognition device 1 based on deep learning, for example, a hard disk of the face recognition device 1 based on deep learning.
  • the memory 11 may also be an external storage device of the face recognition device 1 based on deep learning, such as a plug-in hard disk equipped on the face recognition device 1 based on deep learning, and a smart media card (Smart Media Card). , SMC), Secure Digital (SD) card, Flash Card, etc.
  • the memory 11 may also include both an internal storage unit of the face recognition apparatus 1 based on deep learning and an external storage device.
  • the memory 11 can be used not only to store application software and various data installed in the face recognition device 1 based on deep learning, such as the code of the face recognition program 01 based on deep learning, etc., but also to temporarily store the output or The data to be output.
  • the processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip, and is used to run the program code or processing stored in the memory 11 Data, such as the face recognition program 01 based on deep learning.
  • CPU central processing unit
  • controller microcontroller
  • microprocessor or other data processing chip
  • the communication bus 13 is used to realize the connection and communication between these components.
  • the network interface 14 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is usually used to establish a communication connection between the device 1 and other electronic devices.
  • the device 1 may also include a user interface.
  • the user interface may include a display (Display) and an input unit such as a keyboard (Keyboard).
  • the optional user interface may also 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, organic light-emitting diode) touch device, etc.
  • the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the face recognition device 1 based on deep learning and to display a visualized user interface.
  • FIG. 3 only shows the deep learning-based face recognition device 1 with components 11-14 and the deep-learning-based face recognition program 01. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute The definition of the face recognition device 1 based on deep learning may include fewer or more components than shown, or a combination of certain components, or different component arrangements.
  • the memory 11 stores a face recognition program 01 based on deep learning; the processor 12 implements the following steps when executing the face recognition program 01 based on deep learning stored in the memory 11:
  • the several face image databases include ORL face database, Yale face database, AR face database, and/or FERET face database, etc.
  • the Yale face database includes 15 people, including 11 photos per person, and each photo has changes in lighting conditions, changes in facial expressions, etc.
  • the FERET face database is Counterdrug Technology Transfer Program ( CTTP)
  • CTTP Counterdrug Technology Transfer Program
  • a face database collection activity of Face Recognition Technology Face Recognition Technology, FERET for short
  • the FERET face database includes a general face database and a general test standard.
  • the same face picture includes pictures of different expressions, lighting, postures and age groups.
  • this application uses the Urllib module of python to read web page data, such as reading the web page of the FERET face database, and capture the face image data in the web page of the FERET face database, and combine these The data composes the original face image set.
  • the Urllib module reads web pages such as Yale face database, AR face database, etc., and captures the face image data before placing it in the original face image set.
  • this application composes several Gabor filters into a Gabor filter bank, and after the Gabor filter bank receives the original face image set, the Gabor filter bank is in turn with those in the original face image set.
  • the picture is subjected to the first convolution operation to obtain Gabor features, and the Gabor features obtained from each first convolution operation are combined into a set to obtain the face feature set.
  • O y, u, v (x 1 , x 2 ) is the Gabor feature
  • M(x 1 , x 2 ) is the pixel value coordinates of the picture in the original face image set
  • ⁇ y, u, v (z) is the convolution function
  • z is the convolution operator
  • y, u, v represent the three components of the picture
  • y is the brightness of the picture
  • u, v are the chromaticity of the picture.
  • the preferred embodiment of this application selects 40 Gabor filters to form a Gabor filter bank.
  • the 40 Gabor filters form a Gabor filter bank to read an image of the original face image set and compare it with the Gabor filter bank.
  • the filter bank performs the first convolution operation to obtain Gabor features, and the feature dimension of each Gabor feature is 40, and so on, the Gabor features form the face feature set.
  • the change from the original face image to the Gabor feature is shown in Figure 2.
  • the downsampling technology dimensionality reduction processing includes the first feature dimensionality reduction and the second feature dimensionality reduction.
  • the first feature dimensionality reduction is to sequentially extract Gabor features from the face feature set, and based on a sliding window with a matrix dimension of 2*2, from left to right and from top to bottom in the extracted Gabor A mean value sampling with a step length of 2 is performed on the feature, whereby the feature dimension of the extracted Gabor feature is reduced to 1/4 of the original dimension, and the feature dimension becomes 10, and the first feature dimensionality reduction is completed.
  • the feature dimension of the Gabor feature is reduced to 1/4 of the original dimension, and then an RBM model is connected to perform the second feature reduction.
  • the RBM is an energy model (Energy based model, EBM), which is derived from Evolved from the physical energy model, the RBM model receives input data and solves the probability distribution of the input data according to an energy function, and obtains output data after optimization based on the probability distribution.
  • EBM Energy model
  • the second feature reduction uses the face feature set after the first feature reduction as the input data of the RBM model.
  • the feature dimension of the output feature of the RBM model is 5.
  • the dimensionality reduction processing reduces the feature dimension of Gabor features from 40 to 5, and so on to process each Gabor feature and finally compose the output dimensionality reduction feature into a face feature vector set.
  • the pre-built convolutional neural network includes a sixteen-layer convolutional layer, a sixteen-layer pooling layer, and a fully connected layer.
  • the convolutional neural network receives the face feature vector set, Input the face feature vector set to the sixteen-layer convolutional layer and the sixteen-layer pooling layer to perform a second convolution operation and a maximum pooling operation, and then input to the fully connected layer;
  • the fully connected layer is combined with the activation function to calculate the training value, and the training value is input into the loss function of the model training layer.
  • the loss function calculates the loss value, and judges the loss value and the preset value. The size relationship of the threshold value, until the loss value is less than the preset threshold value, the convolutional neural network exits training.
  • ⁇ ' is the output data
  • ⁇ is the input data
  • k is the size of the convolution kernel
  • s is the stride of the convolution operation
  • p is the data zero-filling matrix
  • the maximum pooling operation is to select a matrix in the matrix The largest value in the data replaces the entire matrix
  • the activation function is:
  • n is the size of the original picture set
  • y t is the training value
  • ⁇ t is the original picture set
  • the preset threshold is generally set at 0.01.
  • the deep learning-based face recognition program can also be divided into one or more modules, and the one or more modules are stored in the memory 11 and are executed by one or more processors ( This embodiment is executed by the processor 12) to complete this application.
  • the module referred to in this application refers to a series of computer program instruction segments that can complete specific functions, and is used to describe how a face recognition program based on deep learning is based on deep learning. The execution process in the face recognition device.
  • FIG. 4 is a schematic diagram of the program modules of the face recognition program based on deep learning in an embodiment of the face recognition device based on deep learning of this application
  • the face recognition program based on deep learning The recognition program can be divided into a source data receiving module 10, a feature extraction module 20, a model training module 30, and a face recognition result output module 40.
  • the source data receiving module 10 is used to obtain face image data from web pages based on crawler technology to form an original face image set.
  • the feature extraction module 20 is configured to: extract the face features of the original face image set according to the Gabor filter to obtain a face feature set, and perform dimensionality reduction processing on the face feature set according to a downsampling technique to form a face feature Vector set.
  • the model training module 30 is configured to: input the face feature vector set into a pre-built convolutional neural network model for training, and exit training when the loss function value in the convolutional neural network is less than a preset threshold.
  • the face recognition result output module 40 is configured to receive a face picture of the user, and input the face picture of the user into the convolutional neural network for face recognition, and output the recognition result.
  • the above-mentioned source data receiving module 10, feature extraction module 20, model training module 30, face recognition result output module 40, and other program modules that implement functions or operation steps when executed are substantially the same as those in the foregoing embodiment, and will not be repeated here.
  • an embodiment of the present application also proposes a computer-readable storage medium that stores a face recognition program based on deep learning, and the face recognition program based on deep learning can be used by one or more Each processor executes to achieve the following operations:
  • a picture of a user's face is received, and the picture of the user's face is input to the convolutional neural network for face recognition, and the recognition result is output.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Biology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Collating Specific Patterns (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un procédé et un appareil de reconnaissance faciale basée sur l'apprentissage profond et un support de stockage lisible par ordinateur, qui se rapportent à la technologie de l'intelligence artificielle. Le procédé consiste à : obtenir des données d'image de visage à partir d'une page web sur la base d'une technologie de robot d'indexation, et constituer un ensemble d'images de visage d'origine (S1); extraire des caractéristiques de visage de l'ensemble d'images de visage d'origine en fonction d'un filtre de Gabor pour obtenir un ensemble de caractéristiques de visage, et effectuer un traitement de réduction de dimensions sur l'ensemble de caractéristiques de visage selon une technique de sous-échantillonnage pour former un ensemble de vecteurs de caractéristiques de visage (S2); entrer l'ensemble de vecteurs de caractéristiques de visage dans un modèle de réseau neuronal convolutif pré-construit pour l'apprentissage jusqu'à ce qu'une valeur de fonction de perte dans un réseau neuronal convolutif soit inférieure à un seuil prédéfini, puis quitter l'apprentissage (S3); et recevoir une image de visage d'utilisateur, entrer l'image de visage d'utilisateur dans le réseau neuronal convolutif pour une reconnaissance faciale, et délivrer en sortie un résultat de reconnaissance (S4). Le procédé décrit peut mettre en oeuvre une reconnaissance faciale efficace et précise.
PCT/CN2019/116934 2019-07-19 2019-11-10 Procédé et appareil de reconnaissance faciale basée sur l'apprentissage profond, et support de stockage lisible par ordinateur WO2021012494A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910658687.0 2019-07-19
CN201910658687.0A CN110516544B (zh) 2019-07-19 2019-07-19 基于深度学习的人脸识别方法、装置及计算机可读存储介质

Publications (1)

Publication Number Publication Date
WO2021012494A1 true WO2021012494A1 (fr) 2021-01-28

Family

ID=68623300

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/116934 WO2021012494A1 (fr) 2019-07-19 2019-11-10 Procédé et appareil de reconnaissance faciale basée sur l'apprentissage profond, et support de stockage lisible par ordinateur

Country Status (2)

Country Link
CN (1) CN110516544B (fr)
WO (1) WO2021012494A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112862095A (zh) * 2021-02-02 2021-05-28 浙江大华技术股份有限公司 基于特征分析的自蒸馏学习方法、设备以及可读存储介质
CN113033406A (zh) * 2021-03-26 2021-06-25 睿云联(厦门)网络通讯技术有限公司 基于深度可分离圆心差分卷积的人脸活体检测方法及系统
CN113807217A (zh) * 2021-09-02 2021-12-17 浙江师范大学 人脸表情识别模型训练、识别方法、系统、装置及介质
CN116453201A (zh) * 2023-06-19 2023-07-18 南昌大学 基于相邻边缘损失的人脸识别方法及系统

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111523094B (zh) * 2020-03-25 2023-04-18 平安科技（深圳）有限公司 深度学习模型水印嵌入方法、装置、电子设备及存储介质
CN111597896B (zh) * 2020-04-15 2024-02-20 卓望数码技术（深圳）有限公司 异常人脸的识别方法、识别装置、识别设备和存储介质
CN111814735A (zh) * 2020-07-24 2020-10-23 深圳市爱深盈通信息技术有限公司 基于人脸识别的取票方法、装置、设备及存储介质
CN112651342A (zh) * 2020-12-28 2021-04-13 中国平安人寿保险股份有限公司 人脸识别方法、装置、电子设备及存储介质
CN113378660B (zh) * 2021-05-25 2023-11-07 广州紫为云科技有限公司 一种低数据成本的人脸识别的方法及装置
CN114707997A (zh) * 2021-07-23 2022-07-05 山东浪潮爱购云链信息科技有限公司 一种防止招投标恶意竞争的方法、存储介质
CN116912918B (zh) * 2023-09-08 2024-01-23 苏州浪潮智能科技有限公司 一种人脸识别方法、装置、设备及计算机可读存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106127159A (zh) * 2016-06-28 2016-11-16 电子科技大学 一种基于卷积神经网络的性别识别方法
CN107392183A (zh) * 2017-08-22 2017-11-24 深圳Tcl新技术有限公司 人脸分类识别方法、装置及可读存储介质
CN109272039A (zh) * 2018-09-19 2019-01-25 北京航空航天大学 一种基于无人机的水坝周边异常监测方法与装置
US20190205517A1 (en) * 2017-12-29 2019-07-04 KeyLemon S.A. Method used in a mobile equipment with a Trusted Execution Environment for authenticating a user based on his face

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100846500B1 (ko) * 2006-11-08 2008-07-17 삼성전자주식회사 확장된 가보 웨이브렛 특징 들을 이용한 얼굴 인식 방법 및장치
CN107423701B (zh) * 2017-07-17 2020-09-01 智慧眼科技股份有限公司 基于生成式对抗网络的人脸非监督特征学习方法及装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106127159A (zh) * 2016-06-28 2016-11-16 电子科技大学 一种基于卷积神经网络的性别识别方法
CN107392183A (zh) * 2017-08-22 2017-11-24 深圳Tcl新技术有限公司 人脸分类识别方法、装置及可读存储介质
US20190205517A1 (en) * 2017-12-29 2019-07-04 KeyLemon S.A. Method used in a mobile equipment with a Trusted Execution Environment for authenticating a user based on his face
CN109272039A (zh) * 2018-09-19 2019-01-25 北京航空航天大学 一种基于无人机的水坝周边异常监测方法与装置

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112862095A (zh) * 2021-02-02 2021-05-28 浙江大华技术股份有限公司 基于特征分析的自蒸馏学习方法、设备以及可读存储介质
CN112862095B (zh) * 2021-02-02 2023-09-29 浙江大华技术股份有限公司 基于特征分析的自蒸馏学习方法、设备以及可读存储介质
CN113033406A (zh) * 2021-03-26 2021-06-25 睿云联(厦门)网络通讯技术有限公司 基于深度可分离圆心差分卷积的人脸活体检测方法及系统
CN113807217A (zh) * 2021-09-02 2021-12-17 浙江师范大学 人脸表情识别模型训练、识别方法、系统、装置及介质
CN113807217B (zh) * 2021-09-02 2023-11-21 浙江师范大学 人脸表情识别模型训练、识别方法、系统、装置及介质
CN116453201A (zh) * 2023-06-19 2023-07-18 南昌大学 基于相邻边缘损失的人脸识别方法及系统
CN116453201B (zh) * 2023-06-19 2023-09-01 南昌大学 基于相邻边缘损失的人脸识别方法及系统

Also Published As

Publication number Publication date
CN110516544B (zh) 2024-04-09
CN110516544A (zh) 2019-11-29

Similar Documents

Publication Publication Date Title
WO2021012494A1 (fr) Procédé et appareil de reconnaissance faciale basée sur l'apprentissage profond, et support de stockage lisible par ordinateur
WO2020199468A1 (fr) Procédé et dispositif de classification d'image et support de stockage lisible par ordinateur
WO2019174130A1 (fr) Procédé de reconnaissance de facture, serveur et support de stockage lisible par ordinateur
WO2019109526A1 (fr) Procédé et dispositif de reconnaissance de l'âge de l'image d'un visage, et support de stockage
WO2020164270A1 (fr) Procédé, système et appareil de détection de piéton sur la base d'un apprentissage profond et support d'informations
WO2019196308A1 (fr) Dispositif et procédé de génération de modèle de reconnaissance faciale, et support d'informations lisible par ordinateur
WO2019095571A1 (fr) Procédé d'analyse d'émotion de figure humaine, appareil, et support d'informations
WO2019033571A1 (fr) Procédé de détection de point de caractéristique faciale, appareil et support de stockage
WO2016150240A1 (fr) Procédé et appareil d'authentification d'identité
WO2019033569A1 (fr) Procédé d'analyse du mouvement du globe oculaire, dispositif et support de stockage
US11886492B2 (en) Method of matching image and apparatus thereof, device, medium and program product
CN111989689A (zh) 用于识别图像内目标的方法和用于执行该方法的移动装置
US10032091B2 (en) Spatial organization of images based on emotion face clouds
CN109637664A (zh) 一种bmi评测方法、装置及计算机可读存储介质
Vazquez-Fernandez et al. Built-in face recognition for smart photo sharing in mobile devices
WO2020253508A1 (fr) Procédé et appareil de détection de cellule anormale, et support d'informations lisible par ordinateur
WO2020248848A1 (fr) Procédé et dispositif de détermination intelligente de cellule anormale, et support d'informations lisible par ordinateur
WO2019033570A1 (fr) Procédé d'analyse de mouvement labial, appareil et support d'informations
WO2016106966A1 (fr) Procédé d'étiquetage de personnages, terminal et support de stockage
CN106056083B (zh) 一种信息处理方法及终端
WO2020253304A1 (fr) Dispositif de reconnaissance faciale et procédé de traitement d'image, modèle d'extraction de caractéristique et support de stockage
WO2019033568A1 (fr) Procédé de saisie de mouvement labial, appareil et support d'informations
WO2019033567A1 (fr) Procédé de capture de mouvement de globe oculaire, dispositif et support d'informations
CN111178195A (zh) 人脸表情识别方法、装置及计算机可读存储介质
CN110335206B (zh) 智能滤镜方法、装置及计算机可读存储介质

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: 19938534

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: 19938534

Country of ref document: EP

Kind code of ref document: A1