WO2020248780A1 - Procédé et appareil de test de corps vivant, dispositif électronique et support de stockage lisible - Google Patents

Procédé et appareil de test de corps vivant, dispositif électronique et support de stockage lisible Download PDF

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WO2020248780A1
WO2020248780A1 PCT/CN2020/091047 CN2020091047W WO2020248780A1 WO 2020248780 A1 WO2020248780 A1 WO 2020248780A1 CN 2020091047 W CN2020091047 W CN 2020091047W WO 2020248780 A1 WO2020248780 A1 WO 2020248780A1
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Prior art keywords
video
living body
probability
frame
sample
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PCT/CN2020/091047
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English (en)
Chinese (zh)
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王鹏
姚聪
卢江虎
李念
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北京迈格威科技有限公司
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    • 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
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive

Definitions

  • the embodiments of the present application relate to the field of data processing technology, and in particular, to a living body detection method, device, electronic device, and readable storage medium.
  • identification technology in the field of data processing technology is widely used in security, finance and other fields, such as face recognition, palmprint recognition or fingerprint recognition based access control unlocking, mobile phone unlocking, remote payment, remote account opening, etc.
  • identification technology Security is getting more and more attention. For example, people will pay attention to how to determine that the recognition object comes from a real person when the recognition object is recognized through the device. For this reason, related technologies have proposed live detection methods.
  • the detection method proposed by related technologies is: first, the object to be detected is required to complete specified facial actions such as opening the mouth and blinking in front of the camera, and the camera captures the specified facial actions. Based on the face image, the processor determines whether the object to be detected in the face image is a living body.
  • facial actions such as opening the mouth and blinking will affect the accuracy of face recognition and reduce the user experience.
  • live detection is performed based on a single image, and the accuracy of live detection is low.
  • the embodiments of the present application provide a living body detection method, device, electronic device, and readable storage medium, aiming to improve the accuracy of living body detection.
  • the first aspect of the embodiments of the present application provides a living body detection method, the method including:
  • the object to be detected is a living body.
  • the method further includes:
  • determining whether the object to be detected is a living body includes:
  • the object to be detected is a living body.
  • the method further includes:
  • the respective characteristics of the multiple frames of video images are spliced to obtain the characteristics of the video, and the video characteristics are used to characterize the inter-frame correlation.
  • determining whether the object to be detected is a living body according to the second probability and the first probability corresponding to each of the multiple frames of video images includes:
  • the second probability and the corresponding probability, and the first probability and the corresponding weight corresponding to each of the multiple frames of video images it is determined whether the object to be detected is a living body.
  • the method further includes:
  • sample video set where the sample set includes a plurality of sample videos carrying tags, and the tags carried by the sample videos represent whether the sample videos are videos collected for a living body;
  • the characteristics of the sample video image of the frame are input into the first fully connected layer of the model to be trained, and the third probability corresponding to the sample video image of the frame is obtained.
  • the third probability characterizes whether the sample video image of the frame is derived from a live body Video of
  • determining the first probability that the frame of video image represents whether the object to be detected is a living body according to the characteristics of the frame of video image includes:
  • the feature of the frame of video image is input into the first fully connected layer of the living body detection model to determine the first probability that the frame of video image represents whether the object to be detected is a living body.
  • the method further includes:
  • Inputting the third probability corresponding to each of the multiple frames of sample video images into the second fully connected layer of the model to be trained to obtain whether the sample video is an estimated probability of a video collected from a living body includes:
  • the fourth probability and the third probability corresponding to the multi-frame sample video image are input into the second fully connected layer of the model to be trained to obtain the estimated probability of whether the sample video is a video collected by a living body .
  • determining whether the object to be detected is a living body according to the first probability corresponding to each of the multiple frames of video images includes:
  • the first probability corresponding to each of the multiple frames of video images is input into the second fully connected layer of the living body detection model to determine whether the object to be detected is a living body.
  • the method further includes:
  • a second aspect of the embodiments of the present application provides a living body detection device, which includes:
  • the first extraction module is configured to extract multiple frames of video images from the video collected for the object to be detected
  • the first determining module is configured to, for each frame of video image in the multiple frames of video images, determine the first probability that the frame of video image represents whether the object to be detected is a living body according to the characteristics of the frame of video image;
  • the second determining module is configured to determine whether the object to be detected is a living body according to the first probability corresponding to each of the multiple frames of video images.
  • the device further includes:
  • a third determining module configured to determine, according to the inter-frame correlation of the multiple frames of video images, the second probability that the inter-frame correlation characterizes whether the object to be detected is a living body
  • the second determining module includes:
  • the first determination submodule is configured to determine whether the object to be detected is a living body according to the second probability and the first probability corresponding to each of the multiple frames of video images.
  • the device further includes:
  • the first splicing module is used to splice the respective characteristics of the multiple frames of video images to obtain the characteristics of the video, and the video characteristics are used to characterize the inter-frame correlation.
  • the first determining submodule includes:
  • An allocation subunit configured to allocate weights to the first probabilities corresponding to the second probability and the multiple frames of video images, wherein the weight corresponding to the second probability is greater than the weight corresponding to each of the first probabilities;
  • the determining subunit is configured to determine whether the object to be detected is a living body according to the second probability and its corresponding probability, and the first probability and its corresponding weight corresponding to each of the multiple frames of video images.
  • the device further includes:
  • the first obtaining module is configured to obtain a sample video set, the sample set includes a plurality of sample videos carrying tags, and the tags carried by the sample videos indicate whether the sample video is a video collected for a living body;
  • the second extraction module is configured to extract a multi-frame sample video image from the sample video with a mark for each sample video with a mark included in the sample video set;
  • the first input module is configured to input each frame of the sample video image in the multi-frame sample video image into the convolutional layer of the model to be trained to obtain the characteristics of the frame sample video image;
  • the second input module is used to input the characteristics of the frame sample video image into the first fully connected layer of the model to be trained to obtain a third probability corresponding to the frame sample video image, and the third probability represents the frame sample video image Whether it comes from a video collected from a living body;
  • the third input module is configured to input the third probability corresponding to each of the multi-frame sample video images into the second fully connected layer of the model to be trained to obtain an estimate of whether the sample video is a video collected by a living body Probability
  • the second obtaining module is configured to establish a loss function according to the estimated probability and the third probability corresponding to each of the multi-frame sample video images, so as to update the model to be trained and obtain a live detection model;
  • the first determining module includes:
  • the first input submodule is configured to input each frame of the video image in the multi-frame video image into the convolutional layer of the living body detection model to obtain the characteristics of the frame of video image;
  • the second input submodule is configured to input the characteristics of the frame of video image into the first fully connected layer of the living body detection model to determine the first probability that the frame of video image represents whether the object to be detected is a living body.
  • the device further includes:
  • the second splicing module is used to splice the respective characteristics of the multiple frames of sample video images to obtain the characteristics of the sample video;
  • the fourth input module is configured to input the characteristics of the sample video into the third fully connected layer of the model to be trained to obtain the fourth probability of whether the sample video is a video collected for a living body;
  • the third input module includes:
  • the third input sub-module is used to input the fourth probability and the third probability corresponding to each of the multi-frame sample video images into the second fully connected layer of the model to be trained to obtain whether the sample video is for a living body The estimated probability of the captured video.
  • the third determining module includes:
  • the fourth input sub-module is configured to input the first probability corresponding to each of the multiple frames of video images into the second fully connected layer of the living body detection model to determine whether the object to be detected is a living body.
  • the device further includes:
  • the third obtaining module is used to obtain the video captured by the video capturing device when the object to be detected is in a silent state.
  • a third aspect of the embodiments of the present application provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the method described in the first aspect of the present application are implemented.
  • a fourth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor implements the method described in the first aspect of the present application when executed A step of.
  • the living body detection method provided in this application, by extracting multiple frames of video images from the video collected for the object to be detected, for each frame of video image, according to the characteristics of the frame of video image, it is determined whether the frame of video image represents the object to be detected It is the first probability of a living body, and finally, according to the determined multiple first probabilities, it is comprehensively determined whether the object to be detected is a living body.
  • the living body detection method provided in this application is based on a video collected for the object to be detected, the living body detection is performed. Specifically, extract multiple frames of video images from the video, use multiple frames of video images to characterize the video, and then for each frame of video image, according to the characteristics of the frame of video image, determine whether the frame of video image represents whether the object to be detected is a living body Finally, according to the determined multiple first probabilities, comprehensively determine whether the object to be detected is a living body. Compared with the prior art that performs live body detection for a single image, this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • the living body detection method provided by the present application extracts multiple frames of video images from the video collected by the object to be detected, the redundant information of the video can be reduced, thereby reducing the amount of calculation and improving the detection efficiency.
  • the living body detection method provided by the present application does not require the subject to be detected to complete specified facial actions such as opening mouth and blinking in front of the camera, which can not only avoid the impact of facial actions on the accuracy of face recognition, but also make it unnecessary for users to make In the case of designated facial movements, complete the living body detection, thereby improving the user experience.
  • FIG. 1 is a schematic diagram of a training process of a living body detection model in an embodiment of the present application
  • FIG. 2 is a flowchart of a living body detection method proposed in an embodiment of the present application.
  • FIG. 3 is another flowchart of the living body detection method proposed by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a living body detection device provided by an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the device needs to collect the user's fingerprint or palm print, or needs to capture the user's face or palm print and other identification objects. Take the face or palm print of the photographed object as an example, in order to prevent the attacker from showing the face photo or palm print photo of another person to the camera, causing the attacker to pass the verification without the permission of others.
  • unauthorized access to another person s account or account, it is necessary to perform a live detection of the face or palm print in the photo taken by the camera to determine whether it is from a real person, that is, to determine whether it is from a living body.
  • a living body judgment method provided by related technologies: First, the object to be detected is required to complete specified facial actions such as opening mouth and blinking in front of the lens.
  • the lens collects a face image for the specified facial action, and the processor judges the face image based on the face image. Whether the object to be detected in the face image is a living body.
  • facial actions such as opening the mouth and blinking will affect the accuracy of face recognition and reduce the user experience.
  • live detection is performed based on a single image, and the accuracy of live detection is low.
  • the applicant proposes to perform live detection based on a video collected for the object to be detected.
  • this application extracts multiple frames of video images from the video, and then for each frame of video image, according to the characteristics of the frame of video image, it is determined whether the frame of video image characterizing the object to be detected is the first of a living body. Probability, and finally according to the determined multiple first probabilities, comprehensively determine whether the object to be detected is a living body.
  • this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • the applicant first constructed the model to be trained, and trained the model to be trained based on the sample video set to obtain a live detection model (for example : The first living body detection model or the second living body detection model described below), the applicant uses the living body detection model to perform part or all of the steps in the above method.
  • a live detection model for example : The first living body detection model or the second living body detection model described below
  • FIG. 1 is a schematic diagram of a training process of a living body detection model in an embodiment of the present application.
  • the living body detection model includes: a convolutional layer, a first fully connected layer, and a second fully connected layer.
  • the convolutional layer can specifically adopt a convolutional neural network.
  • the model to be trained also includes a convolutional layer, a first fully connected layer, and a second fully connected layer. After training, the model parameters of the model to be trained are updated and adjusted, and finally the live detection model is obtained.
  • the sample video set is a sample video set about a human face as an example, and each step is introduced.
  • the type of sample video set is not limited to the sample video set about human faces. For example, it can also be a sample video set about palm prints. If the training model is trained based on the sample video set about palm prints, the final result is
  • the living body detection model can be used for living body detection for palmprint videos.
  • S110 Obtain a sample video set, the sample set includes a plurality of sample videos carrying tags, and the tags carried by the sample videos indicate whether the sample videos are videos collected for a living body.
  • part or all of the sample videos in the sample video set may be videos collected by the video collection device when the training participant is in a silent state.
  • the training participants When collecting videos of the training participants, the training participants only need to look at the video collection device, and the training participants are not required to complete designated facial actions such as opening their mouths, blinking eyes, and reading aloud in front of the camera.
  • a silent video can be taken for the face of each training participant among multiple training participants (real people).
  • the duration of the video can be controlled within 1 to 3 seconds, and such videos taken for real people can be labeled ,
  • Make this kind of video carry a tag, which indicates that this kind of video is a video collected from a living body.
  • You can shoot a video for each of the non-living bodies such as multiple printed photos, screen photos, and masks.
  • the length of the video can be controlled within 1 to 3 seconds.
  • This type of video shot for non-living bodies can be marked to make This type of video carries a tag that indicates that the video is not a video collected from a living body.
  • S120 For each sample video with a mark included in the sample video set, extract multiple frames of sample video images from the sample video with a mark.
  • each sample video carrying a mark it can be first divided into N sub-segments, and then a frame of RGB video image is extracted from each sub-segment as the sample video image, and finally from each sample carrying the mark A total of N frames of sample video images can be extracted from the video.
  • S130 Input each frame of the sample video image in the multi-frame sample video image into the convolutional layer of the model to be trained to obtain the characteristics of the frame sample video image.
  • N frames of sample video images can be sequentially input to the convolutional neural network of the model to be trained, and the convolutional neural network outputs a three-dimensional convolution feature for each frame of sample video image, that is, the feature of the sample video image of the frame.
  • the convolutional neural network outputs a three-dimensional convolution feature for each frame of sample video image, that is, the feature of the sample video image of the frame.
  • multiple frames of sample video images can share a convolutional neural network, and each frame of sample video images in the multiple frame of sample video images can also correspond to a convolutional neural network. Therefore, the number of convolutional neural networks included in the model to be trained may be one or multiple.
  • S140 Input the features of the sample frame of video image into the first fully connected layer of the model to be trained to obtain a third probability corresponding to the sample frame of video image, and the third probability represents whether the sample frame of video image is derived from a living body The captured video.
  • each feature of the N frames of sample video images can be sequentially input to the first fully connected layer, and the first fully connected layer outputs a probability vector of the shape (x, y) for the features of each frame of sample video image, namely
  • x represents the probability that the sample video image of the frame is derived from a video collected for a living body
  • y represents the probability that the sample video image of the frame is derived from a video collected for a non-living body.
  • S150 Input the third probability corresponding to each of the multiple frames of sample video images into the second fully connected layer of the model to be trained to obtain the estimated probability of whether the sample video is a video collected for a living body.
  • N third probabilities corresponding to N frames of sample video images can be input to the second fully connected layer, and the second fully connected layer outputs a probability vector of the shape (X, Y) for the N third probabilities, namely The estimated probability, where X represents the probability that the sample video is a video collected for a living body, and Y represents the probability that the sample video is a video collected for a non-living body.
  • S160 Establish a loss function according to the estimated probability and the third probability corresponding to each of the multi-frame sample video images, so as to update the model to be trained and obtain a live detection model.
  • the loss function is established, based on the estimated probability, such as a probability vector of (X, Y), and a third probability corresponding to each of the multi-frame sample video images, such as a probability vector of (x, y), the loss function is established, Using the gradient descent method, update the parameters of the model to be trained, and put the updated model to be trained into the next round of training. After multiple rounds of training, a live detection model is obtained. For example, after a fixed M round of training, such as 1000 rounds of training, the training ends, and the living body detection model is obtained. For another example, when the loss function of multiple consecutive rounds of training reflects that the model to be trained can accurately predict whether the sample video is a live body, the training is ended to obtain a live body detection model.
  • the implementation manner of establishing the loss function may be: comparing the N third probabilities with the markers carried by the sample video respectively, where each third probability is the prediction result, and the marker carried by the sample video represents the real situation, and the Nth probabilities are obtained.
  • a comparison result, the N first comparison results can represent the accuracy of the sample video prediction of the model to be trained in this round of training.
  • the second comparison result can also represent the model to be trained. In rounds of training, the accuracy of the sample video prediction.
  • the parameters of the model to be trained are adjusted to update the model to be trained. Put the updated model to be trained into the next round of training, and after multiple rounds of training, a live detection model is obtained.
  • the convergence speed of the model to be trained can be accelerated, and on the other hand, the model not only Based on the prediction accuracy of the model to be trained on the sample video, the parameters of the model to be trained are updated, and the parameters of the model to be trained are updated based on the prediction accuracy of each frame of the sample video image of the model to be trained, so that the final live detection
  • the model can output more accurate prediction results.
  • the first living body detection model is obtained.
  • some or all of the following steps can be performed: Extract from the video collected for the object to be detected Generate multiple frames of video images, and then for each frame of video image, according to the characteristics of the frame of video image, determine the first probability that the frame of video image represents whether the object to be detected is a living body, and finally according to the determined multiple first probabilities, Comprehensively determine whether the object to be detected is a living body.
  • the applicant of this application found that in addition to the multi-frame video image extracted from a video can characterize the video, the inter-frame correlation of the multi-frame video image can also be used to characterize the video. If multiple frames of video images and the inter-frame correlation of the multi-frame video images are used to characterize the video at the same time, the inter-frame correlation can be further introduced when performing living detection, which can further improve the accuracy of living detection.
  • the applicant further proposes that the inter-frame correlation is introduced into the living body detection method, and the second probability that the inter-frame correlation characterizes whether the object to be detected is a living body is determined first, and then the second probability and the multi-frame video image The first probability corresponding to each determines whether the object to be detected is a living body. Thereby further improving the accuracy of living body detection.
  • the applicant first constructed the model to be trained, and trained the model to be trained based on the sample video set to obtain the live detection model.
  • the applicant uses the living body detection model to perform some or all of the steps in the method further proposed above.
  • the living body detection model may also include: a feature combination module and a third fully connected layer. It should be understood that the model structure of the model to be trained constructed by the applicant in advance is the same as the model structure of the living body detection model shown in FIG. 1.
  • the model to be trained may also include a feature combination module and a third fully connected layer, and after training , The model parameters of the model to be trained are updated and adjusted, and finally the live detection model is obtained.
  • an embodiment of the present application further proposes steps S142, S144, and S150' on the basis of steps S110, S120, S130, S140, and S160.
  • steps S130, S140, S142, S144, S150' and S160 are the steps of each round of training in multiple rounds:
  • S142 Splicing each feature of the multiple frames of sample video images to obtain the feature of the sample video.
  • the feature combination module can be used to stack the N three-dimensional convolution features to obtain a new three-dimensional convolution feature as the sample video Features.
  • the features of the sample video can characterize the inter-frame correlation of multiple frames of sample video images. For example, after step S130, 8 36*36*25 convolution features are obtained, and after these 8 36*36*25 convolution features are stacked, a 36*36*200 convolution feature is obtained. The 36*200 convolution feature is used as the feature of the sample video.
  • S144 Input the characteristics of the sample video into the third fully connected layer of the model to be trained, and obtain a fourth probability of whether the sample video is a video collected for a living body.
  • the characteristics of the sample video can be input to the third fully connected layer, and the third fully connected layer outputs the probability vector of the shape (x', y') for the characteristics of the sample video, that is, the fourth probability, where x 'Represents the probability that the sample video is a video collected for a living body, and y'represents the probability that the sample video is a video collected for a non-living body.
  • S150' Input the third probability corresponding to each of the multiple frames of sample video images into the second fully connected layer of the model to be trained to obtain the estimated probability of whether the sample video is a video collected by a living body.
  • This step specifically includes: inputting the fourth probability and the third probability corresponding to each of the multi-frame sample video images into the second fully connected layer of the model to be trained to obtain whether the sample video is collected for a living body The estimated probability of the video.
  • the fourth probability corresponding to the sample video and the N third probabilities corresponding to the N frames of the sample video image can be input to the second fully connected layer, and the second fully connected layer outputs a form such as (X , Y) is the estimated probability, where X represents the probability that the sample video is a video collected for a living body, and Y represents the probability that the sample video is a video collected for a non-living body.
  • a second living body detection model is obtained.
  • some or all of the following steps can be executed : Extract multiple frames of video images from the video collected for the object to be detected, for each frame of video image, according to the characteristics of the frame of video image, determine the first probability that the frame of video image represents whether the object to be detected is a living body; and The inter-frame correlation of the multi-frame video image determines the second probability that the inter-frame correlation characterizes whether the object to be detected is a living body, and finally the object to be detected is determined according to the second probability and the first probability corresponding to each of the multi-frame video images Whether it is alive.
  • the foregoing embodiments of the present application mainly propose two training processes for the model to be trained based on a sample video set, and finally obtain the first living body detection model and the second living body detection model respectively.
  • this application will focus on the live body detection method, and schematically introduce how to apply the first live body detection model or the second live body detection model to the live body detection method.
  • FIG. 2 is a flowchart of a living body detection method proposed in an embodiment of the present application. As shown in Figure 2, the method includes the following steps:
  • the object to be detected refers to an object that needs to be detected whether it is a living body.
  • the object to be detected is not limited to only the face to be detected.
  • the object to be detected may also be a palm print or fingerprint to be detected. If the object to be detected is a palm print, the video collected for the object to be detected is a video shot for the palm print to be detected.
  • the method further includes: obtaining a video collected by the video collection device when the object to be detected is in a silent state.
  • the video collected for the object to be detected may be a silent video collected for the object to be detected.
  • a video is collected for the object to be detected, for example, a short video of 1 to 3 seconds is collected for the object to be detected.
  • the user when collecting a video from a user, the user only needs to look at the video capture device, and the user is not required to complete specified facial actions such as opening mouth, blinking, and reading aloud in front of the camera, which not only avoids the accuracy of face recognition by facial actions The impact of this can also enable users to complete living body detection without having to make specified facial actions, thereby improving user experience.
  • the extracted video images when multiple frames of video images are extracted from a video, they may be extracted at equal intervals between frames, and the extracted video images may be RGB images. For example, for a piece of video, for example, every 5 frames of video image, one frame of video image is extracted. Taking a video including 48 frames of video images as an example, the extracted video images of each frame are: frame 6, frame 12, frame 18, frame 24, frame 30, frame 36, frame 42 and frame 48 frames.
  • the video when extracting multiple frames of video images from a video, the video may be divided into multiple sub-segments first, and then one frame of video image is extracted from each sub-segment.
  • the video is equally divided into N sub-segments, and for each sub-segment, one frame of video image is randomly extracted therefrom, or one frame of video image is extracted from the middle of the sub-segment.
  • multiple frames of video images are extracted at equal intervals between frames, or by dividing the video into multiple sub-segments, and then extracting one frame of video images from each sub-segment, so that the extracted multiple frames of video images are evenly distributed Among the video images in the video, multiple frames of video images can more accurately characterize the content of the video, thereby further improving the accuracy of living body detection.
  • the multi-frame video image extracted from the video collected for the object to be detected is characterized by using the multi-frame video image to characterize the video, so that the living body detection method proposed in this application is based on the video. Detection.
  • this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • this application extracts multiple frames of video images from the video collected by the object to be detected, the redundant information of the video can be reduced, thereby reducing the amount of calculation and improving the detection efficiency.
  • S24 For each frame of video image in the multiple frames of video images, determine the first probability that the frame of video image represents whether the object to be detected is a living body according to the characteristics of the frame of video image.
  • the feature of the video image may be a convolution feature.
  • the first living body detection model obtained through training may be used. Specifically, each frame of the video image in the multi-frame video image is input into the convolutional layer of the living body detection model to obtain the characteristics of the frame of video image; and then the characteristics of the frame video image are input into the living body detection model.
  • the first fully connected layer is used to determine the first probability that the frame of video image represents whether the object to be detected is a living body.
  • the first probability corresponding to each frame of video image may be a probability vector in the form of (x, y), where x represents the probability that the object to be detected is a living body, and y represents the probability that the object to be detected is inanimate.
  • the feature of each frame of video image can be obtained through a convolutional neural network, or other image feature extraction methods can be used to extract the feature of each frame of video image. Then, the feature of each frame of video image is input into the first fully connected layer of the first living body detection model to determine the first probability that the frame of video image represents whether the object to be detected is a living body.
  • S26 Determine whether the object to be detected is a living body according to the first probability corresponding to each of the multiple frames of video images.
  • the first living body detection model obtained through training may be used to determine whether the object to be detected is a living body.
  • the first probability corresponding to each of the multiple frames of video images is input into the second fully connected layer of the living body detection model to determine whether the object to be detected is a living body.
  • each frame of video image in the multiple frames of video images is input to the convolutional layer in the living body detection model, and the convolutional layer outputs the characteristics of each frame of video image; the characteristics of each frame of video image are then input to the living body detection model
  • the first fully connected layer the first fully connected layer outputs the first probability corresponding to each frame of video image; the first probability corresponding to each frame of video image is then input to the second fully connected layer of the live detection model, the second fully connected
  • the layer outputs an estimated probability, which is a comprehensive probability that characterizes whether the object to be detected is a living body.
  • the estimated probability may be a probability vector in the form of (X, Y), where X represents the comprehensive probability that the object to be detected is a living body, and Y represents the comprehensive probability that the object to be detected is a non-living body.
  • the average value of the multiple first probabilities may be calculated to determine whether the object to be detected is a living body.
  • the first probability corresponding to multiple frames of video images is a probability vector of the shape (x, y), where x represents the probability that the object to be detected is a living body, and y represents the probability that the object to be detected is non-living.
  • the probability vectors corresponding to the 8 frames of video images extracted from the video are: (35.9,13.0), (43.2,5.6), (34.7,14.3), (44.6,5.4), (58.6,2.1), (41.8) ,6.7), (29.2,17.8), (21.4,22.8), based on the above 8 probability vectors, the integrated average probability vector is calculated as (38.7,11.0), where the probability that the object to be detected is alive is greater than that of the object to be detected The probability that the object is a non-living body, determines that the object to be detected is a living body.
  • a live body detection is performed based on a video collected for the object to be detected. Specifically, extract multiple frames of video images from the video, use multiple frames of video images to characterize the video, and then for each frame of video image, according to the characteristics of the frame of video image, determine whether the frame of video image represents whether the object to be detected is a living body Finally, according to the determined multiple first probabilities, comprehensively determine whether the object to be detected is a living body. Compared with the prior art that performs live body detection for a single image, this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • the inter-frame correlation of multiple frames of video images can also be used to characterize the piece of video. If multiple frames of video images and multiple The inter-frame correlation of a frame video image characterizes the video. When performing live detection, further introducing inter-frame correlation can further improve the accuracy of live detection.
  • FIG. 3 is another flowchart of the living body detection method proposed in an embodiment of the present application. As shown in Figure 3, the method includes the following steps:
  • S24 For each frame of video image in the multiple frames of video images, determine the first probability that the frame of video image represents whether the object to be detected is a living body according to the characteristics of the frame of video image.
  • the inter-frame correlation refers to: information between frames of a multi-frame video image. Specifically, for each frame of video images in a multi-frame video image, the feature of the frame of video image can be extracted, and the respective features of the multi-frame video image can be spliced to obtain the feature of the video, and the video feature is used to characterize all the video images.
  • the inter-frame correlation for each frame of video images in a multi-frame video image, the feature of the frame of video image can be extracted, and the respective features of the multi-frame video image can be spliced to obtain the feature of the video, and the video feature is used to characterize all the video images.
  • multiple frames of video images can be input to the convolutional layer of the second living detection model obtained through training, and the convolutional layer outputs the three-dimensional convolution feature of each frame of video image, and the three-dimensional convolution feature is It is a feature of video images. Then stacking a plurality of three-dimensional convolution features to obtain a new three-dimensional convolution feature as the feature of the video, and the video feature is used to characterize the inter-frame correlation.
  • the convolutional layer of the living body detection model outputs 8 36*36*25 convolutional features
  • the feature combination module of the living body detection model combines these 8 36*36*25
  • a 36*36*200 convolution feature is obtained, and the 36*36*200 convolution feature is used as the video feature.
  • the video feature characterizing the inter-frame correlation may be input to the second living body detection model obtained through training.
  • the third fully connected layer outputs a second probability that characterizes whether the object to be detected is a living body according to the video feature.
  • the second probability may be a probability vector in the form of (x', y'), where x'represents the probability that the object to be detected is a living body, and y'represents the probability that the object to be detected is non-living.
  • S26' Determine whether the object to be detected is a living body according to the second probability and the first probability corresponding to each of the multiple frames of video images.
  • the second living body detection model obtained through training can be used to Determine whether the object to be detected is alive.
  • the second probability of the inter-frame correlation characterizing whether the object to be detected is a living body, and the first probability corresponding to each of the multiple frames of video images may be input to the second fully connected layer of the second living body detection model, and the second fully connected layer
  • the connection layer outputs an estimated probability, which is a comprehensive probability that characterizes whether the object to be detected is a living body.
  • the estimated probability may be a probability vector in the form of (X, Y), where X represents the comprehensive probability that the object to be detected is a living body, and Y represents the comprehensive probability that the object to be detected is a non-living body.
  • determining whether the object to be detected is a living body according to the second probability and the first probability corresponding to each of the multiple frames of video images may specifically include:
  • S26'-1 Assign weights to the first probabilities corresponding to the second probability and the multiple frames of video images, wherein the weight corresponding to the second probability is greater than the weight corresponding to each of the first probabilities;
  • S26'-2 Determine whether the object to be detected is a living body according to the second probability and its corresponding probability, and the first probability and its corresponding weight corresponding to each of the multiple frames of video images.
  • the convolutional layer For example, after 8 frames of video images are input to the living body detection model, they pass through the convolutional layer and the first fully connected layer of the living body detection model, and output the first probabilities corresponding to each of the 8 frames of video images.
  • the 8 first probabilities are: (35.9 ,13.0), (43.2,5.6), (34.7,14.3), (44.6,5.4), (58.6,2.1), (41.8,6.7), (29.2,17.8), (21.4,22.8).
  • the output features of the convolutional layer for each frame of video image are all 36*36*25 convolution features.
  • the feature combination module stacks these 8 36*36*25 convolution features to obtain a 36*36*200
  • the convolution feature of 36*36*200 is used as the feature of the video, which represents the inter-frame correlation of multiple frames of video images.
  • the second probability is output, assuming that the second probability is (50.1, 3.5).
  • the second probability and the first probability corresponding to the multiple frames of video images are assigned weights.
  • the weight assigned to the second probability is 1/2
  • the weight assigned to each first probability is 1/16.
  • the weighted average probability is calculated according to the second probability and its corresponding probability, and the first probability and its corresponding weight corresponding to each of the multiple frames of video images, and whether the object to be detected is a living body is determined according to the weighted average probability.
  • the weighted average probability obtained is (44.4, 7.3), where the probability that the object to be detected is alive is greater than the probability that the object to be detected is non-living, and it is determined that the object to be detected is alive.
  • step S26'-1 and step S26'-2 By performing step S26'-1 and step S26'-2 to assign a larger weight to the second probability, it is possible to highlight the proportion of the inter-frame correlation of multi-frame video images in characterizing a piece of video information, as well as in the live detection process It plays a role in improving the accuracy of detection, thereby further improving the accuracy of live detection.
  • FIG. 4 is a schematic diagram of a living body detection device provided by an embodiment of the present application. As shown in Figure 4, the device includes:
  • the first extraction module 41 is configured to extract multiple frames of video images from the video collected for the object to be detected;
  • the first determining module 42 is configured to, for each frame of video image in the multi-frame video image, determine the first probability that the frame of video image represents whether the object to be detected is a living body according to the characteristics of the frame of video image;
  • the second determining module 43 is configured to determine whether the object to be detected is a living body according to the first probability corresponding to each of the multiple frames of video images.
  • the device further includes:
  • a third determining module configured to determine, according to the inter-frame correlation of the multiple frames of video images, the second probability that the inter-frame correlation characterizes whether the object to be detected is a living body
  • the second determining module includes:
  • the first determination submodule is configured to determine whether the object to be detected is a living body according to the second probability and the first probability corresponding to each of the multiple frames of video images.
  • the device further includes:
  • the first splicing module is used to splice the respective characteristics of the multiple frames of video images to obtain the characteristics of the video, and the video characteristics are used to characterize the inter-frame correlation.
  • the first determining submodule includes:
  • An allocation subunit configured to allocate weights to the first probabilities corresponding to the second probability and the multiple frames of video images, wherein the weight corresponding to the second probability is greater than the weight corresponding to each of the first probabilities;
  • the determining subunit is configured to determine whether the object to be detected is a living body according to the second probability and its corresponding probability, and the first probability and its corresponding weight corresponding to each of the multiple frames of video images.
  • the device further includes:
  • the first obtaining module is configured to obtain a sample video set, the sample set includes a plurality of sample videos carrying tags, and the tags carried by the sample videos indicate whether the sample video is a video collected for a living body;
  • the second extraction module is configured to extract a multi-frame sample video image from the sample video with a mark for each sample video with a mark included in the sample video set;
  • the first input module is configured to input each frame of the sample video image in the multi-frame sample video image into the convolutional layer of the model to be trained to obtain the characteristics of the frame sample video image;
  • the second input module is used to input the characteristics of the frame sample video image into the first fully connected layer of the model to be trained to obtain a third probability corresponding to the frame sample video image, and the third probability represents the frame sample video image Whether it comes from a video collected from a living body;
  • the third input module is configured to input the third probability corresponding to each of the multi-frame sample video images into the second fully connected layer of the model to be trained to obtain an estimate of whether the sample video is a video collected by a living body Probability
  • the second obtaining module is configured to establish a loss function according to the estimated probability and the third probability corresponding to each of the multi-frame sample video images, so as to update the model to be trained and obtain a live detection model;
  • the first determining module includes:
  • the first input submodule is configured to input each frame of the video image in the multi-frame video image into the convolutional layer of the living body detection model to obtain the characteristics of the frame of video image;
  • the second input submodule is configured to input the characteristics of the frame of video image into the first fully connected layer of the living body detection model to determine the first probability that the frame of video image represents whether the object to be detected is a living body.
  • the device further includes:
  • the second splicing module is used to splice the respective characteristics of the multiple frames of sample video images to obtain the characteristics of the sample video;
  • the fourth input module is configured to input the characteristics of the sample video into the third fully connected layer of the model to be trained to obtain the fourth probability of whether the sample video is a video collected for a living body;
  • the third input module includes:
  • the third input sub-module is used to input the fourth probability and the third probability corresponding to each of the multi-frame sample video images into the second fully connected layer of the model to be trained to obtain whether the sample video is for a living body The estimated probability of the captured video.
  • the third determining module includes:
  • the fourth input sub-module is configured to input the first probability corresponding to each of the multiple frames of video images into the second fully connected layer of the living body detection model to determine whether the object to be detected is a living body.
  • the device further includes:
  • the third obtaining module is used to obtain the video captured by the video capturing device when the object to be detected is in a silent state.
  • an embodiment of the present application also provides an electronic device.
  • a schematic structural diagram of the electronic device is shown in FIG. 4.
  • the electronic device 7000 includes at least one processor 7001, a memory 7002, and a bus 7003.
  • the memory 7001 is electrically connected to the storage 7002; the memory 7002 is configured to store at least one computer-executable instruction, and the processor 7001 is configured to execute the at least one computer-executable instruction, so as to execute any one in the first embodiment of the present application. Steps of any living body detection method provided by an embodiment or any optional implementation.
  • the processor 7001 may be an FPGA (Field-Programmable Gate Array) or other devices with logic processing capabilities, such as MCU (Microcontroller Unit), CPU (Central Process Unit, Central Processing Unit) ).
  • FPGA Field-Programmable Gate Array
  • MCU Microcontroller Unit
  • CPU Central Process Unit
  • Central Processing Unit Central Processing Unit
  • the living body detection method provided by the present application is based on a video collected for the object to be detected, the living body detection is performed. Specifically, extract multiple frames of video images from the video, use multiple frames of video images to characterize the video, and then for each frame of video image, according to the characteristics of the frame of video image, determine whether the frame of video image represents whether the object to be detected is a living body Finally, according to the determined multiple first probabilities, comprehensively determine whether the object to be detected is a living body. Compared with the prior art that performs live body detection for a single image, this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • the living body detection method provided by the present application extracts multiple frames of video images from the video collected by the object to be detected, the redundant information of the video can be reduced, thereby reducing the amount of calculation and improving the detection efficiency.
  • the living body detection method provided by this application does not require the object to be detected to complete specified facial actions such as opening mouth and blinking in front of the camera, which can not only avoid the impact of facial actions on the accuracy of face recognition, but also make it unnecessary for users to do In the case of designated facial movements, complete the living body detection, thereby improving the user experience.
  • the embodiment of the present application also provides a computer-readable storage medium, such as the memory 7002 in FIG. 4, in which a computer program 7002a is stored, which is used to implement the implementation of the present application when executed by a processor. Steps of any embodiment or any living body detection method in Example 1.
  • the computer-readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM ( Random Access Memory), EPROM (Erasable Programmable Read-Only Memory, Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), Flash memory, Magnetic Card or light card. That is, a readable storage medium includes any medium that stores or transmits information in a readable form by a device (for example, a computer).
  • the living body detection method provided by the present application is based on a video collected for the object to be detected, the living body detection is performed. Specifically, extract multiple frames of video images from the video, use multiple frames of video images to characterize the video, and then for each frame of video image, according to the characteristics of the frame of video image, determine whether the frame of video image represents whether the object to be detected is a living body Finally, according to the determined multiple first probabilities, comprehensively determine whether the object to be detected is a living body. Compared with the prior art that performs live body detection for a single image, this application uses video as the basis to perform live body detection, and the detection result is more accurate.
  • the living body detection method provided by the present application extracts multiple frames of video images from the video collected by the object to be detected, the redundant information of the video can be reduced, thereby reducing the amount of calculation and improving the detection efficiency.
  • the living body detection method provided by the present application does not require the subject to be detected to complete specified facial actions such as opening mouth and blinking in front of the camera, which can not only avoid the impact of facial actions on the accuracy of face recognition, but also make it unnecessary for users to make In the case of designated facial movements, complete the living body detection, thereby improving the user experience.

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Abstract

Un mode de réalisation de la présente invention relève du domaine technique du traitement de données. L'invention concerne un procédé et un appareil de test de corps vivant, un dispositif électronique et un support de stockage lisible. Le procédé de test de corps vivant consiste à : extraire de multiples trames d'images vidéo d'une vidéo collectée pour un objet à tester ; pour chaque trame d'une image vidéo dans les multiples trames d'images vidéo, déterminer, selon des caractéristiques de la trame d'une image vidéo, une première probabilité que la trame d'une image vidéo représente si l'objet à tester est un corps vivant ; et déterminer si l'objet à tester est un corps vivant, selon des premières probabilités correspondant respectivement aux multiples trames d'images vidéo. Selon le procédé de test de corps vivant selon la présente invention, la précision de test d'un corps vivant peut être améliorée.
PCT/CN2020/091047 2019-06-13 2020-05-19 Procédé et appareil de test de corps vivant, dispositif électronique et support de stockage lisible WO2020248780A1 (fr)

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