WO2021051602A1 - Procédé et système de reconnaissance faciale à base de mot de passe lu avec les lèvres, dispositif, et support de stockage - Google Patents

Procédé et système de reconnaissance faciale à base de mot de passe lu avec les lèvres, dispositif, et support de stockage Download PDF

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WO2021051602A1
WO2021051602A1 PCT/CN2019/118281 CN2019118281W WO2021051602A1 WO 2021051602 A1 WO2021051602 A1 WO 2021051602A1 CN 2019118281 W CN2019118281 W CN 2019118281W WO 2021051602 A1 WO2021051602 A1 WO 2021051602A1
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lip
password
lip language
language
video
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Chinese (zh)
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张国辉
董洪涛
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships

Definitions

  • This application relates to the field of biometric recognition technology, and in particular to a face recognition method, system, device, and storage medium based on lip ciphers.
  • the password used is a combination of numbers, characters or other symbols.
  • the user must accurately remember the password used and enter it correctly to be successfully identified. Therefore, the traditional password identification method is easy to forget and remember incorrectly. , The operation is cumbersome and other disadvantages.
  • fingerprint recognition, face recognition, and iris recognition methods can overcome the above-mentioned shortcomings of traditional passwords that need to be memorized and input one by one, but they also have the following shortcomings: 1. Simple fingerprint recognition and face recognition, facing the attack of copying fingerprints and photos The risk of using copied fingerprints and static photos will deceive fingerprint recognition and face recognition; 2. The security of iris recognition is higher, but there is the problem of expensive equipment and higher economic costs.
  • This application provides a face recognition method, system, electronic device, and computer readable storage medium based on lip language passwords, which mainly use the dlib+Resnet face key point detection model to screen lip language images; through two-way LSTM
  • the lip language recognition model completes the classification of lip language images, and realizes the technical effect of using lip language as a password for face recognition.
  • the present application provides a face recognition method based on lip language password, which is applied to an electronic device.
  • the method includes: S110: Obtain a password-reading video of the subject to be tested; S120: Use a Resnet-based face detection model to The video is detected frame by frame, and the lip language images of consecutive frames in the time period from the start time to the end time of the subject to be tested reading the password in the video are obtained; S130, the lip language images of the consecutive frames in the time period , Determine the lip language feature set in the time period; S140, input the lip language feature set into the trained two-way LSTM-based lip language recognition model to obtain the predicted value of the lip language password; S150, if the lip language password If the predicted value is consistent with the password stored in the lip language recognition model, it is confirmed that the subject to be tested is recognized by the lip language password.
  • the present application provides a lip language password-based face recognition system, including a lip language feature set acquisition unit, a lip language password prediction value acquisition unit, and a password determination unit; wherein the lip language feature set acquisition unit , Used to obtain the password-reading video of the subject to be tested; the video is detected frame by frame through the face detection model based on Resnet, and the time period from the starting point of the subject to be tested to the end point in the video is obtained continuously Frame of lip language images; determine the lip language feature set in the time period according to the lip language images of consecutive frames in the time period; the lip language password prediction value acquisition unit is configured to combine the lip language features Input the trained lip language recognition model based on two-way LSTM to obtain the predicted value of the lip language password; the password determination unit is used to compare the predicted value of the lip language password with the password stored in the lip language recognition model Yes, if they are the same, confirm that the subject to be tested is recognized by the lip code.
  • the lip language feature set acquisition unit Used to obtain the password-reading video of the subject to
  • the present application also provides an electronic device, the electronic device comprising: a memory, a processor, the memory stores a lip-password-based facial recognition program, the lip-password-based facial recognition program
  • the following steps are implemented: S110, obtaining a password-reading video of the subject to be tested; S120, detecting the video frame by frame through the Resnet-based face detection model to obtain the password-reading video of the subject to be tested in the video
  • S130 Determine the lip language feature set in the time period according to the lip images of the consecutive frames in the time period; S140.
  • the language feature set is input into the trained lip language recognition model based on the two-way LSTM to obtain the predicted value of the lip language password; S150. If the predicted value of the lip language password is consistent with the password stored in the lip language recognition model, confirm the test to be tested The subject is identified by a lip code.
  • the present application also provides a computer-readable storage medium storing a computer program, the computer program including a lip-based cipher-based face recognition program, the lip-based
  • the face recognition program of the language password is executed by the processor, the steps of the above-mentioned face recognition method based on the lip language password are realized.
  • the face recognition method, system, electronic device and computer readable storage medium based on lip language password proposed in this application classify lip language images by using 2D convolution model + bidirectional LSTM model + Softmax layer + optimized network layer, Train a model; among them, the 2D convolution model is used to extract human lips features; the two-way LSTM model is used to connect multiple lip pictures in the video in series to extract temporal features; the Softmax layer is used to output lip password prediction values; optimization
  • the network layer is used to input the predicted value of the lip language password into the loss function for iterative training until the value of the loss function reaches the set threshold; finally obtain a face recognition model based on the lip language password; use the face recognition model based on the lip language password
  • the recognition model performs lip-password recognition, which can achieve the technical effects of low cost and high recognition accuracy.
  • FIG. 1 is a flowchart of a preferred embodiment of a face recognition method based on lip language password according to this application;
  • FIG. 2 is a flowchart of a preferred embodiment of a method for constructing a lip language recognition model based on a two-way LSTM according to the present application;
  • FIG. 3 is a schematic diagram of the principle of a face recognition method based on lip language passwords in this application;
  • FIG. 4 is a schematic structural diagram of a preferred embodiment of a face recognition system based on lip language passwords according to this application;
  • FIG. 5 is a schematic structural diagram of a preferred embodiment of the electronic device of this application.
  • FIG. 1 shows a flowchart of a preferred embodiment of a face recognition method based on lip language passwords according to an embodiment of the present application.
  • the method can be executed by a device, and the device can be implemented by software and/or hardware.
  • This application uses the dlib+Resnet face key point detection model to screen lip language images; the two-way LSTM model completes the classification of lip language images, thereby realizing the use of lip language as a password for face recognition.
  • Resnet residual Network
  • Resnet network is a new type of network structure with powerful functions. Resnet network can be seen as a combination of parallel and serial modules. Resnet is a better network for classification problems in the ImageNet competition. It has a variety of structural forms, including Resnet-34, Resnet-50, Resnet-101, and Resnet-152; the design of the Resnet network structure follows two design rules: (1) For the same output feature map size, the layers have the same number of filters; (2) If the feature map size is halved, the number of filters is doubled in order to maintain the time complexity of each layer. The first is the skip connection method, and the second is the use of the Batch Normalization layer.
  • the Resnet-based face detection algorithm needs to use windows with different sizes and positions to slide in the image, and then determine whether there is a face in the window.
  • HOG human oriented gradient
  • dlib convolutional neural networks for face detection.
  • the face recognition method based on lip ciphers in this application uses the interface of the trained Resnet model in dlib, and this interface returns a 128-dimensional face feature vector.
  • the face recognition method based on lip language password includes step S110-step S150.
  • the subject to be tested is the user who wants to perform password detection.
  • the user who needs to perform password detection needs to read out the preset voice password to pass the password detection.
  • the detection device needs to obtain the action video of the subject to be tested reading the password.
  • the user randomly selects 4-6 numbers as the password.
  • the user faces the camera and reads the above numbers at a constant speed.
  • the speed is about one number per second.
  • the reading can be in Mandarin, dialect, and English. Wait. When collecting information, it can be repeated 4-10 times.
  • the user must remember the 4-6 digits set by himself as a password. The next time you enter, just read out the above password to complete the identification.
  • S120 Perform frame-by-frame detection on the video by using a Resnet-based face detection model, and obtain lip language images of consecutive frames in the time period from the starting point to the ending point when the subject to be tested reads the password in the video.
  • the lip language image is parsed through the feature point model of the dlib database to obtain lip feature information; and the time period from the start time to the end time of the subject to be tested reading the password in the video is analyzed by analyzing the reading Acquire the sound waveform of the password video.
  • the video is detected frame by frame through the Resnet-based face detection model, the lip position is determined by the feature point model of the dlib database, and several frames of lip images are cut out; the digital password video is obtained by analyzing the sound waveform. According to the start time and end time of the sound, continuous frames of lip language images are filtered out according to the above start time and end time.
  • the face detection model detects the face of the video frame by frame, and then confirms the position of the lips according to the feature point model of dlib, thereby cutting out the lip picture from the video.
  • An exemplary description is as follows: For example, if the user's video reads 1234, the sample is a number of frames of lip pictures that are cut out, and the label is the pinyin corresponding to 1234: er sansi, and the different pinyins are separated by spaces.
  • the lip language recognition model determines the image of a continuous frame of the face in the video, so that the number is recognized through the lip movement, and the recognized number is compared with the password stored in the background. If they are consistent, then by.
  • the method for preprocessing the lip language image in step S130 includes: S310, normalizing the lip language image; S320, storing the normalized lip language image as a data set sample in a specified format ;
  • the storage format of the data set samples is [number of samples, data sequence length, image length, image width, image depth].
  • S140 Input the lip language feature set into a trained lip language recognition model based on two-way LSTM to obtain a lip language password prediction value.
  • Fig. 2 is a flowchart of a preferred embodiment of a method for constructing a lip language recognition model based on a two-way LSTM according to an embodiment of the present application. As shown in Fig. 2, the method for constructing a lip language recognition model based on a two-way LSTM includes: step S210- Step S240.
  • S210 Construct an initial network layer for acquiring lip features of the subject to be tested; the initial network layer is a 2D convolutional network.
  • S220 Construct a bidirectional LSTM layer on the initial network layer for extracting temporal features in the training set data.
  • Mouth vocalization is a dynamic process, and you need to look at the frames before and after the current frame to get the hidden layer information of the current frame more accurately. That is to say, the two-way LSTM layer considers the influence of "past” and "future” on the current frame.
  • the step size is the number of lip images extracted from the video.
  • Softmax used to classify a segment of lip motion images, there are a total of 0-9, a total of 10 categories.
  • the output layer using Softmax has multiple units. In fact, as many categories as we have, there will be as many units as possible. In this example, we have 10 categories, so there are also 10 neural units. Represents these 10 categories. Under the action of Softmax, each neural unit will calculate the probability that the current sample belongs to this category.
  • the neural network of the lip recognition model includes: a three-layer 2D convolutional network, a two-layer bidirectional LSTM network, and a fully connected layer with an activation function of Softmax, and finally a logical prediction layer (that is, an optimized network) Floor).
  • S240 Construct an optimized network layer on the Softmax layer; wherein the optimized network layer is used to input the predicted value of the lip password into a loss function for iterative training until the value of the loss function reaches a set threshold.
  • the training set data is input to the neural network, and the predicted value of the lip password is obtained through layer-by-layer calculation, and the predicted value of the lip password and the real label are input into the loss function, and Loss is calculated. Propagate the revised model parameters.
  • the loss function is used to iteratively train the above neural network model until Loss reaches the set threshold.
  • the bidirectional LSTM-based lip language recognition model of the present application is a bidirectional cyclic neural network.
  • the Forward layer and the Backward layer are jointly connected to the output layer, which contains 6 shared weights w1-w6.
  • the way to adjust the parameters is to use the gradient descent algorithm (Gradientdescent) to adjust the size of the parameters along the gradient direction.
  • the greater the gradient of the activation function the faster the size of w and b are adjusted, and the faster the training will converge.
  • the activation function commonly used in neural networks is the sigmoid function.
  • the forward calculation is performed from time 1 to time t, and the output of the forward hidden layer at each time is obtained and saved.
  • the backward calculation is performed from time t to time 1, and the output of the backward hidden layer at each time is obtained and saved. Finally, at each moment, the final output is obtained by combining the output results of the Forward layer and the Backward layer at the corresponding time.
  • the two-way LSTM network performs better in time series classification tasks. At the same time, it uses time series history and future information, combined with context information, and comprehensively judges the results, and the judgment results are more accurate.
  • Fig. 3 is a schematic diagram of the principle of a face recognition method based on a lip language password according to an embodiment of the present application.
  • the prescribed format is the storage format of the data set sample.
  • the prescribed storage format is [sample number, data sequence length, image length, image width, image depth ⁇ .
  • the above data set is divided into two parts at a ratio of eight to two, one is used as a training set, and the other is used as a test set; among them, the training set is used to train a lip recognition model based on two-way LSTM.
  • the steps of constructing a lip language recognition model based on bidirectional LSTM include: first designing the number of convolution kernels, constructing a 2D convolutional network, then designing the number of hidden layer cells and step size, constructing a bidirectional LSTM network; then constructing the Softmax layer, Softmax The layer is used to use the Softmax function to classify the obtained time characteristics of the password-reading video of the subject to be tested, and finally select the node with the largest probability (that is, the value corresponding to the largest) as the lip password prediction value output.
  • the last step is to use the loss function to optimize the network, and finally through the iterative training of the loss function, the lip recognition model based on two-way LSTM with the best recognition effect is obtained.
  • the lip language recognition model based on two-way LSTM can predict the lip language video.
  • FIG. 4 shows the structure of a preferred embodiment of a face recognition system based on lip ciphers according to the present application.
  • a face recognition system 400 based on a lip language password includes a lip language feature set acquisition unit 410, a lip language password prediction value acquisition unit 420, and a password determination unit 430; wherein,
  • the lip feature set obtaining unit 410 is used to obtain the password reading video of the subject to be tested; the video is detected frame by frame through the Resnet-based face detection model to obtain the starting point of the subject to be tested in the video to read the password
  • the lip language images of consecutive frames in the time period from the time to the end time; and the lip language feature set in the time period is determined according to the lip language images of the consecutive frames in the time period.
  • the lip language password prediction value obtaining unit 420 is configured to input the lip language feature set into a trained lip language recognition model based on two-way LSTM to obtain a lip language password prediction value.
  • the password determination unit 430 is configured to compare the predicted value of the lip language password with the password stored in the lip language recognition model, and if they are consistent, confirm that the subject to be tested is recognized by the lip language password.
  • the lip language feature set acquisition unit 410 includes a video acquisition sub-unit 411, a lip language image acquisition sub-unit 412, and a lip language feature set acquisition sub-unit 413; wherein, the video acquisition sub-unit uses To obtain the password-reading video of the subject to be tested; the lip language image acquisition subunit is used to detect the video frame by frame through the Resnet-based face detection model to obtain the starting point of the subject to be tested in the video to read the password Lip language images of consecutive frames in the time period from time to end time; the lip language feature set acquiring subunit is used to determine the lip language images in the time period according to the lip language images of consecutive frames in the time period Feature set.
  • the lip language feature set acquisition subunit 413 includes an image normalization module and a data set sample acquisition module; wherein the image normalization module is used to normalize the lip language image;
  • the data set sample acquisition module is used to store the normalized lip language image as a data set sample in a prescribed format.
  • the lip language image acquisition sub-unit 412 includes a video detection module and a lip language image acquisition module; wherein, the video detection module is used to perform the video detection on the video through the Resnet-based face detection model. Frame-by-frame detection; the lip language image acquisition module is used to acquire the lip language images of consecutive frames in the time period from the starting point to the ending point of the subject to be tested reading the password in the video.
  • the lip language image acquisition module includes a lip feature information acquisition sub-module and a time point acquisition sub-module; wherein the lip feature information acquisition sub-module is used to analyze the lip language through the feature point model of the dlib database The image acquires lip feature information; the time point acquisition sub-module is used for the time period from the start time to the end time of the subject to be tested for reading the password in the video by parsing the sound waveform of the password reading video.
  • the lip language password prediction value acquisition unit 420 includes a two-way LSTM-based lip language recognition model building module 421; the two-way LSTM-based lip language recognition model building module includes 421 an initial network layer construction sub-module, a two-way LSTM layer The construction sub-module, the Softmax layer construction module and the optimized network layer construction module; wherein the initial network layer construction sub-module is used to construct an initial network layer for obtaining the lip characteristics of the subject to be tested; the initial network layer is 2D Convolutional network; the two-way LSTM layer construction sub-module is used to construct a two-way LSTM layer for extracting temporal features in the training set data on the initial network layer; the Softmax layer construction module is used to The Softmax layer for outputting the predicted value of the lip cipher is constructed on the bidirectional LSTM layer; the optimized network layer construction module is used to construct an optimized network layer on the Softmax layer; wherein, the optimized network layer is used to transfer the The predicted value of the lip password is input to the
  • the Softmax layer construction module includes a lip password prediction value acquisition sub-module; the lip password prediction value acquisition sub-module is used to obtain a lip password prediction value through the temporal feature data extracted by the two-way LSTM layer.
  • the Softmax layer construction module also includes a parameter adjustment sub-module, the parameter adjustment sub-module is used to adjust the weight W and the offset b along the gradient direction by using the activation function sigmoid and the gradient descent algorithm.
  • Fig. 5 shows a schematic diagram of an application environment of a preferred embodiment of a face recognition method based on lip ciphers according to the present application.
  • the electronic device 5 may be a terminal device with arithmetic function, such as a server, a smart phone, a tablet computer, a portable computer, a desktop computer, and the like.
  • the electronic device 5 includes a processor 52, a memory 51, a communication bus 53 and a network interface 54.
  • the memory 51 includes at least one type of readable storage medium.
  • the at least one type of readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card-type memory 51, and the like.
  • the readable storage medium may be an internal storage unit of the electronic device 5, such as a hard disk of the electronic device 5.
  • the readable storage medium may also be the external memory 51 of the electronic device 5, such as a plug-in hard disk equipped on the electronic device 5, or a smart memory card (Smart Media Card, SMC). , Secure Digital (SD) card, Flash Card (Flash Card), etc.
  • SD Secure Digital
  • Flash Card Flash Card
  • the readable storage medium of the memory 51 is generally used to store the lip-password-based face recognition program 50 and the like installed in the electronic device 5.
  • the memory 51 can also be used to temporarily store data that has been output or will be output.
  • the processor 52 may be a central processing unit (CPU), microprocessor or other data processing chip, used to run the program code or processing data stored in the memory 51, for example, to execute lip-based Password facial recognition program 50 etc.
  • CPU central processing unit
  • microprocessor or other data processing chip, used to run the program code or processing data stored in the memory 51, for example, to execute lip-based Password facial recognition program 50 etc.
  • the communication bus 53 is used to realize the connection and communication between these components.
  • the network interface 54 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the electronic device 5 and other electronic devices.
  • a standard wired interface and a wireless interface such as a WI-FI interface
  • FIG. 5 only shows the electronic device 5 with the components 51-54, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
  • the electronic device 5 may also include a user interface.
  • the user interface may include an input unit such as a keyboard (Keyboard), a voice input device such as a microphone (microphone) and other devices with voice recognition functions, and a voice output device such as audio, earphones, etc.
  • the user interface may also include a standard wired interface and a wireless interface.
  • the electronic device 5 may also include a display, and the display may also be referred to as a display screen or a display unit.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device, etc.
  • the display is used to display the information processed in the electronic device 5 and to display a visualized user interface.
  • the electronic device 5 may also include a radio frequency (RF) circuit, a sensor, an audio circuit, etc., which will not be repeated here.
  • RF radio frequency
  • the memory 51 which is a computer storage medium, can store an operating system and a face recognition program 50 based on a lip code; the processor 52 executes the lip based code stored in the memory 51
  • the password facial recognition program 50 implements the following steps: S110, obtain the password-reading video of the subject to be tested; S120, detect the obtained password-reading video frame by frame through the Resnet-based face detection model, and obtain the obtained video
  • S130 according to the lip language images of the consecutive frames in the time period, determine the lip language feature set in the time period S140, input the lip language feature set into a trained lip language recognition model based on two-way LSTM to obtain a lip language password predicted value; S150, if the lip language password predicted value is stored in the lip language recognition model If the passwords are consistent, it is confirmed that the subject to be tested is identified by the lip-password.
  • the facial recognition program 50 based on lip ciphers may also be divided into one or more modules, and the one or more modules are stored in the memory 51 and executed by the processor 52 to complete the application .
  • the module referred to in this application refers to a series of computer program instruction segments that can complete specific functions.
  • the face recognition program 50 based on the lip language password can be divided into: a lip language feature set acquisition unit, a lip language password prediction value acquisition unit, and a password determination unit.
  • the realized functions or operation steps are similar to the above, and will not be described in detail here.
  • an embodiment of the present application also proposes a computer-readable storage medium, the computer-readable storage medium includes a lip-password-based facial recognition program, and the lip-password-based facial recognition program is executed by a processor Realize the following operations: S110. Obtain the password-reading video of the subject to be tested; S120. Detect the video frame by frame through the Resnet-based face detection model to obtain the time from the starting point to the end point of the subject to be tested in the video. Lip language images of consecutive frames in a time period; S130. Determine a lip language feature set in the time period according to the lip language images of consecutive frames in the time period; S140. Input the lip language feature set for training.
  • the lip language recognition model based on the two-way LSTM obtains the lip language password prediction value; S150. If the lip language password prediction value is consistent with the password stored in the lip language recognition model, confirm that the subject to be tested is recognized by the lip language password .
  • the specific implementation of the computer-readable storage medium of the present application is substantially the same as the specific implementation of the above-mentioned face recognition method and electronic device based on the lip-password, and will not be repeated here.

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Abstract

La présente demande appartient au domaine de la technologie de reconnaissance biométrique, et concerne un procédé et un système de reconnaissance faciale à base de mot de passe lu avec les lèvres, un dispositif et un support de stockage. Ledit procédé comprend les étapes consistant à : obtenir une vidéo dans laquelle un corps principal à détecter lit un mot de passe ; effectuer une détection sur la vidéo trame par trame au moyen d'un modèle de détection de visage à base de ResNet, de façon à acquérir des images continues de lèvres dans la vidéo dans une période de temps allant d'un instant de début à un instant de fin au cours de laquelle le corps principal à détecter lit un mot de passe ; déterminer un ensemble de caractéristiques de lèvres dans la période de temps selon les images continues de lèvres dans la période de temps ; entrer l'ensemble de caractéristiques de lèvres dans un modèle de reconnaissance de lèvres entraîné basé sur un LSTM bidirectionnel, de façon à obtenir une valeur de prédiction du mot de passe lu avec les lèvres ; et si la valeur de prédiction du mot de passe lu avec les lèvres est cohérente avec un mot de passe stocké dans le modèle de reconnaissance de lèvres, déterminer que le corps principal à détecter a réussi la reconnaissance de mot de passe lu avec les lèvres. Le procédé de reconnaissance faciale à base de mot de passe lu avec les lèvres selon la présente demande est capable d'obtenir des effets de détection rapide et de précision de détection élevée.
PCT/CN2019/118281 2019-09-19 2019-11-14 Procédé et système de reconnaissance faciale à base de mot de passe lu avec les lèvres, dispositif, et support de stockage Ceased WO2021051602A1 (fr)

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