WO2020187160A1 - Procédé et système de reconnaissance faciale basés sur un réseau neuronal à convolution profonde en cascade - Google Patents

Procédé et système de reconnaissance faciale basés sur un réseau neuronal à convolution profonde en cascade Download PDF

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WO2020187160A1
WO2020187160A1 PCT/CN2020/079281 CN2020079281W WO2020187160A1 WO 2020187160 A1 WO2020187160 A1 WO 2020187160A1 CN 2020079281 W CN2020079281 W CN 2020079281W WO 2020187160 A1 WO2020187160 A1 WO 2020187160A1
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network
face
face recognition
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convolutional neural
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翟新刚
张楠赓
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北京嘉楠捷思信息技术有限公司
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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
    • 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
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation

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  • the field of artificial intelligence technology of the present invention particularly relates to a face recognition method and system based on a cascaded deep convolutional neural network.
  • Face recognition technology is a kind of biometric recognition technology based on the facial feature information of people.
  • the face recognition process is mainly to collect video streams with a camera, automatically detect and track faces in the images, and then perform face recognition on the detected faces.
  • face recognition systems have been widely used in various fields, such as community access control, company attendance, judicial and criminal investigations, etc.
  • face recognition is a kind of multi-task combination work in deep learning, but existing technical solutions often focus on the realization of a certain task, and ignore the relationship between multiple tasks.
  • the main purpose of the present invention is to provide a face recognition method and system based on a cascaded deep convolutional neural network to solve at least one of the above-mentioned problems.
  • a face recognition method based on a cascaded deep convolutional neural network including:
  • the extracting facial features using a cascaded deep convolutional neural network includes:
  • the output of the second network is sent to the third network to extract facial features.
  • the sending the output of the first network to the second network to predict the location of key points on the face includes:
  • the sending the output of the second network to the third network to extract facial features includes:
  • the first network is a face detection network (Face Detection Network, referred to as FDNet)
  • the second network is a key-point detection network (Key-point Detection Network, referred to as KDNet)
  • the third network is a feature Extraction network (Feature Extraction Network, FENet for short).
  • the method before extracting facial features by using the cascaded deep convolutional neural network, the method further includes: collecting facial image data.
  • a face recognition system based on a cascaded deep convolutional neural network including:
  • a feature extraction module for extracting facial features using cascaded deep convolutional neural networks
  • the face recognition module is connected to the feature extraction module and is used to perform face recognition based on the extracted facial features.
  • the feature extraction module includes:
  • the first network is used to receive face image data and predict the regression of the face frame
  • the frame interception unit is used to receive the output of the first network and perform frame interception and size conversion operations
  • the second network is used to receive the output of the frame interception unit and predict the position of key points on the face;
  • the similarity transformation unit is used to receive the output of the second network and perform similarity transformation, mapping and size transformation operations;
  • the third network is used to receive the output of the similarity transformation unit and extract facial features.
  • the first network is a face detection network (Face Detection Network, referred to as FDNet)
  • the second network is a key-point detection network (Key-point Detection Network, referred to as KDNet)
  • the third network is a feature Extraction network (Feature Extraction Network, FENet for short).
  • it further includes a collection module for collecting face image data.
  • the present invention uses a cascaded deep convolutional neural network for feature extraction, and performs face recognition based on the extracted features.
  • Each level of the cascaded deep convolutional neural network only needs to be executed for everyone Once, the control is simple, the amount of calculation is small, and it is easy to accelerate; and extracting facial features through deep learning for face recognition can easily cope with face recognition tasks of various security levels.
  • the present invention uses similar transformations when performing face recognition based on cascaded deep convolutional neural networks, which further reduces the background effect caused by different frame sizes and reduces the demand for the network.
  • the present invention maps face recognition to multiple different deep learning models independently according to different tasks, which has strong replaceability, avoids waste of computing power, and facilitates intuitive determination of the need to upgrade the network part.
  • Fig. 1 is a schematic flow chart of the face recognition method of the present invention.
  • Fig. 2 is a schematic diagram of a frame cut in the face recognition method shown in Fig. 1.
  • FIG. 3 is a flowchart of the face recognition method of the present invention.
  • Fig. 4 is another flowchart of the face recognition method of the present invention.
  • Figure 5 is a flow chart of the inventors extracting facial features.
  • Fig. 6 is a flow chart of predicting the position of key points on a face according to the present invention.
  • Fig. 7 is another flow chart of extracting facial features according to the present invention.
  • Figure 8 is a schematic diagram of the structure of the face recognition system of the present invention.
  • Fig. 9 is a schematic diagram of another structure of the face recognition system of the present invention.
  • Figure 10 is a schematic diagram of the structure of the feature extraction module of the present invention.
  • FIG. 11 is another flowchart of the face recognition method according to the embodiment of the present invention.
  • Face recognition usually includes face detection, face feature extraction, and classification of the extracted face features to complete face recognition.
  • face detection is to find out whether there are one or more faces in a given picture, and return the position and range of each face in the picture. Face detection algorithms are divided into four categories: knowledge-based, feature-based, template matching-based, and appearance-based methods. With the use of DPM (Direct Part Model) algorithm (variable component model) and deep learning Convolutional Neural Networks (CNN), all face detection algorithms can be divided into two categories: (1) Based on Template matching (Based on rigid templates): Among them, there are algorithms (Boosting) + features (Features) and CNN; (2) Based on parts model (Based on parts model).
  • DPM Direct Part Model
  • CNN deep learning Convolutional Neural Networks
  • Facial feature extraction is a process of obtaining facial feature information in the area where the face is located on the basis of face detection.
  • Face feature extraction methods include: Eigenface (Eigenface), Principal Component Analysis (Principal Component Analysis, referred to as PAC).
  • PAC Principal Component Analysis
  • Classification refers to classifying according to type, level or nature, and classifying the extracted features to complete face recognition.
  • Classification methods mainly include: decision tree method, Bayesian method, and artificial neural network.
  • the process of the face recognition method of the present invention is: Pyramid scale transformation is performed on a new picture, and the transformed picture is input into a network to generate a large number of face classification scores.
  • Regression vector with the face rectangle also called box, border, bounding box, window, window, etc.
  • weed out the face rectangle with lower score for example, lower than a threshold M1
  • replace the remaining face rectangle The frame performs non-maximum suppression to obtain the final prediction result; then the predicted result is input into another network, and the face rectangle with a lower score (for example, lower than the threshold M2) is also eliminated, and then non-maximum suppression is used
  • the algorithm filters out the large overlapping face rectangles, displays the key points of the face, and performs feature extraction and face recognition.
  • the face recognition method is introduced by taking the face network (FaceNet) as an example. As shown in Figure 1-2, the face recognition method includes the following steps:
  • FaceNet extracts facial features in two steps:
  • MTCNN Multi-task Cascaded Convolutional Networks
  • the MTCNN predicts the Bounding Box of the face, as shown in Figure 1, including the following sub-steps:
  • the input original image is scaled to various sizes, that is, the original image is subjected to different Scale Resize operations to build an image pyramid, and each layer of the pyramid is sent to the shallow CNN candidate box network (Proposal Network, referred to as PNet) And perform Bounding Box Regression and Non-maximum Suppression (NMS) to quickly generate candidate forms;
  • PNet Proposal Network
  • NMS Non-maximum Suppression
  • a more powerful CNN output network (Output network, referred to as ONet) is used to realize the selection of candidate forms and display the location of five facial key points at the same time.
  • the above method uses MTCNN to predict Bounding Box that requires repeated PNet and RNet multiple times, and the control is relatively complicated and the amount of calculation is large.
  • the Bounding Box predicted by MTCNN is added with a fixed-length Margin and sent to the feature extraction network. Since the Bounding Box of the face in the figure will have various sizes, if a fixed Margin is added to the face of different size, the size will be different. The background information of the face will be very different, so it will weaken the generalization ability of the feature extraction network.
  • the present invention also provides a method for realizing sensorless face recognition based on a cascaded deep convolutional neural network.
  • the face recognition method based on the cascaded deep convolutional neural network of the present invention includes the following steps:
  • S2 Perform face recognition according to the extracted facial features.
  • the present invention uses cascaded deep convolutional neural networks for feature extraction, and performs face recognition based on the extracted features.
  • Each level of network in the cascaded deep convolutional neural network only needs to be executed once for each person, and control Simple, small amount of calculation, easy to accelerate.
  • the face recognition method may further include: S0, collecting facial image data.
  • the extraction of facial features using the cascaded deep convolutional neural network includes:
  • S11 Send the face image data to the first network to predict the regression of the face frame
  • S12 Send the output of the first network to the second network to predict the location of key points on the face;
  • S13 Send the output of the second network to the third network to extract facial features.
  • the cascaded deep convolutional neural network may include three networks, and the three networks form a three-level cascaded deep convolutional neural network; wherein, the first network is a face detection network (Face Detection Network). Network, referred to as FDNet), the second network is a Key-point Detection Network (Key-point Detection Network, referred to as KDNet), and the third network is a Feature Extraction Network (Feature Extraction Network, referred to as FENet).
  • FDNet face detection network
  • KDNet Key-point Detection Network
  • FENet Feature Extraction Network
  • the present invention disassembles the face recognition task and fully considers the relationship between multiple tasks. Specifically, the present invention disassembles the face recognition task
  • the convolutional neural networks provided by deep learning corresponding to the three-level tasks are FDNet, KDNet and FENet.
  • face recognition is independently mapped to multiple different deep learning models according to different tasks, which is highly replaceable, avoids waste of computing power, and facilitates intuitive determination of the need to upgrade the network part.
  • the sending the output of the first network to the second network to predict the location of key points on the face includes:
  • S121 Perform frame interception and size conversion operations on the output of the first network before sending it to the second network;
  • S122 Use the second network to predict the location of key points on the face.
  • the sending the output of the second network to the third network to extract facial features includes:
  • the present invention uses similar transformations to further reduce the background effect caused by different frame sizes, reduce the demand for FDNet, and improve the accuracy of feature extraction .
  • the present invention also provides a face recognition system based on a cascaded deep convolutional neural network.
  • the face recognition system based on a cascaded deep convolutional neural network includes:
  • the feature extraction module 11 is used for extracting facial features using cascaded deep convolutional neural networks.
  • the face recognition module 12 is connected to the feature extraction module 11 and is used to perform face recognition according to the extracted facial features.
  • the face recognition system may further include a collection module 10 for collecting face image data.
  • the feature extraction module 11 is connected to the collection module 10, and is configured to receive face image data sent by the collection module 10, and extract facial features by using a cascaded deep convolutional neural network.
  • the feature extraction module includes:
  • the first network 110 is configured to receive the face image data and predict the regression of the face frame
  • the frame interception unit 111 is configured to receive the output of the first network 110 and perform frame interception and size conversion operations;
  • the second network 112 is configured to receive the output of the frame interception unit 111 and predict the position of key points on the face;
  • the similarity transformation unit 113 is configured to receive the output of the second network 112 and perform similarity transformation, mapping and size transformation operations;
  • the third network 114 is configured to receive the output of the similarity transformation unit 113 and extract facial features.
  • the first network is a face detection network (Face Detection Network, referred to as FDNet)
  • the second network is a key-point detection network (Key-point Detection Network, referred to as KDNet)
  • the third network is a feature extraction network (Feature Extraction Network, referred to as FENet).
  • the face recognition method based on the cascaded deep convolutional neural network specifically includes:
  • Step 1 FDNet is based on YOLO's design idea, using MobileNet as the backbone, directly Bounding Box Regression on the face, and predicting the confidence at the same time; in other words, the task of FDNet is to find The position of all faces in the image, and intercept all face images as the input of KDNet in turn.
  • FDNet includes but is not limited to MobileNet-YOLO.
  • the face is marked in the data set, and the golden truth of the face frame is set as follows:
  • Location is set to among them, Represents the coordinates of the upper left corner of the i-th face frame, Respectively represent the width and height of the face frame;
  • the corresponding YOLO prediction result is x i y i w i h i C i p i ;
  • the MobileNet-YOLO network in the stable state can be used for the prediction of the face frame.
  • Step 2 Based on the output of FDNet, cut out the bounding box, transform the size (Resize) to a fixed size, and send it to KDNet (Keypoints Detection Net) to directly predict the positions of five facial key points; that is, KDNet's task
  • KDNet Keypoints Detection Net
  • this embodiment takes five points as an example (left eye corner, right eye corner, nose, left mouth corner, right mouth corner), traverse all the inputs of FDNet, you can get everyone The key points of the face.
  • KPNet can choose a variety of networks, and only needs to predict the location of key points on the face.
  • the following takes the left eye, right eye, nose, left mouth corner, and right mouth corner of the face as examples to introduce the process of marking the key points of the face in the data set:
  • the face data is preprocessed by the FDNet in the aforementioned step 1, and the face part is cut out by the face frame, and the position of the left eye, right eye, nose, left mouth corner, and right mouth corner of the face is calculated in the face part.
  • Position ratio get five pairs of position coordinates (x, y), x ⁇ [0,1], y ⁇ [0,1]
  • the Sigmoid function is as follows:
  • Step 3 Based on the output of five facial key points in KDNet, perform five-point similarity transformation on the entire frame of image, map it to five points at a fixed golden position, and transform the mapped face image to a fixed size (Resize)
  • the size is sent to FENet (Feature Extraction Net, referred to as FENet) to extract facial features; that is to say, a similar transformation is required between KDNet and FENet as a bridge, and the key point position information obtained by KDNet is compared with the position of golden (Golden).
  • the key point position information obtains the similarity transformation matrix, and then the entire frame image is similarly transformed corresponding to the key points to obtain the similarly transformed face image.
  • the task of FENet is to abstract the face image information into a feature vector representation. This feature Vector representation has the following characteristics: all faces of the same subject can be mapped to similar feature vectors, while the feature vectors obtained by all faces of different subjects are quite different.
  • This embodiment uses a five-point similarity transformation, of course, it is not limited to the five-point similarity transformation.
  • first set the golden positions of the left eye, right eye, nose, left mouth corner, and right mouth corner of the face to resemble the transformed image Take 112*112 as an example, the Golden position can be selected as follows
  • Nose_g [56.0252, 71.7366]
  • Golden key point Landmark_Golden [le_g, re_g, nose_g, l_mouth_g, r_mouth_g]
  • Landmark_get [le, re, nose, l_mouth, r_mouth]
  • Landmark_Get Landmark_Golden can perform similar transformations, as follows:
  • the FENet includes but is not limited to Mobilefacenets, and AM-softmax Loss can be selected as the loss function.
  • the face recognition method based on the cascaded deep convolutional neural network described in this embodiment uses a cascaded deep convolutional neural network (Cascaded-Deep CNN, referred to as CDCNN) to extract facial features and perform human Face recognition.
  • CDCNN cascaded deep convolutional neural network
  • Each level of CDCNN only needs to be executed once for everyone, which is simple to control, small in calculation, and easy to accelerate; and compared to the aforementioned Bounding Box method of adding Margin, it uses a five-point similar transformation to further reduce
  • the background effect caused by the different sizes of the Bounding Box reduces the demand for FDNet (as long as the five key points of the face are accurate, the face detection box does not have to be generated by the MTCNN network).
  • the face recognition method and system based on the cascaded deep convolutional neural network of the present invention may also include other parts, which are not related to the innovation of the present invention, so they will not be repeated here.
  • modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and/or processes or units are mutually exclusive, any combination can be used to compare all the features of the invention in this specification (including the accompanying claims, abstract and drawings) and any method or method of such invention. All the processes or units of the equipment are combined. Unless expressly stated otherwise, each feature of the invention in this specification (including the accompanying claims, abstract and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.
  • the various component embodiments of the present invention may be implemented by hardware, or by software modules running on one or more processors, or by their combination.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the relevant device according to the embodiments of the present invention.
  • DSP digital signal processor
  • the present invention can also be implemented as a device or device program (for example, a computer program and a computer program product) for executing part or all of the methods described herein.
  • Such a program for realizing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such signals can be downloaded from Internet websites, or provided on carrier signals, or provided in any other form.
  • ordinal numbers used in the specification and claims such as “first”, “second”, “third”, etc., are used to modify the corresponding elements, and they do not imply or represent that the elements have any
  • the ordinal number does not represent the order of a certain element and another element, or the order in the manufacturing method. The use of these ordinal numbers is only used to make it clear that one element with a certain name can be made clear from another element with the same name distinguish.

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Abstract

L'invention concerne un procédé et un système de reconnaissance faciale basés sur un réseau neuronal à convolution profonde en cascade. Le procédé de reconnaissance faciale basé sur un réseau neuronal à convolution profonde en cascade consiste à : extraire une caractéristique faciale au moyen d'un réseau neuronal à convolution profonde en cascade ; et effectuer une reconnaissance faciale en fonction de la caractéristique faciale extraite. Le procédé et le système de reconnaissance faciale basés sur un réseau neuronal à convolution profonde en cascade de l'invention sont simples à commander, ce qui requiert une petite quantité de calcul et facilite l'accélération.
PCT/CN2020/079281 2019-03-15 2020-03-13 Procédé et système de reconnaissance faciale basés sur un réseau neuronal à convolution profonde en cascade WO2020187160A1 (fr)

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