Living body face detection method based on WLD-TOP (Weber Local Descriptor-Three Orthogonal Planes)

Abstract

The invention discloses a living body face detection method based on WLD-TOP (Weber Local Descriptor-Three Orthogonal Planes). The method comprises the following steps: (1) training stage: reading a training set video, performing face region detection on each frame, converting the frames into a gray level facial image frame sequence to construct a three-dimensional image matrix, constructing a filtering template, calculating WLD features, generating a WLD-TOP feature vector, and inputting the feature vector into an SVM classifier for training to establish an SVM model; and (2) testing stage: for an image sequence under test, performing face detection on each frame, converting the frames into a gray level facial image sequence, constructing a three-dimensional image matrix and a filtering template, calculating WLD features, generating a WLD-TO feature vector, and feeding the WLD-TOP feature vector into a trained SVM model to obtain a living body face detection result. The Weber law is adopted on the basis of the LBP-TOP, so that the size relationships between neighborhood pixels and a center pixel are reflected, and the differences between the neighborhood pixels and the center pixel are quantified. Thus, the features of a descriptor are more complete.

Description

technical field [0001] The invention relates to the research field of human face detection, in particular to a living human face detection method based on WLD-TOP. Background technique [0002] Face recognition technology compares and analyzes the biological characteristics of human faces to identify people's identities. Face recognition technology has made great progress in the past few decades, and face recognition products have been used in many occasions such as access control, monitoring of important places, and entry and exit. One of the advantages of face recognition technology is that it can automatically identify targets without supervision, but it also leaves hidden dangers. If criminals use users' photos or even videos to easily fool the face recognition system, it will cause harm and serious threats. to social security and stability. [0003] Common face spoofing attacks include photo attacks and video attacks. Photo attacks have the user's facial features, wh...

AI Technical Summary

Problems solved by technology

Photo attacks have the user's facial features, while video attacks have more dynamic features of legitimate users, such as blinking and changes in facial expressions, which are more deceptive and seriously affect the accuracy of the face recognition system.

[0004] The current living face detection methods mainly include the following types: one is based on the texture structure analysis method, which extracts relevant texture features for discrimination by analyzing the difference between the three-dimensional living face and the retaken face imaging; the other is based on In the method of facial motion information analysis, the essential difference between a living face and a retaken face is that the former is a three-dimensional object, while the latter is a two-dimensional plane structure. There is a second shot of the face, and the motion effects produced by them are completely different; The third is the method based on the analysis of living body feature information. This method analyzes the thermal infrared image of the face, eye blinking and lip movement and other living body features. This method may require some additional detection equipment support, so there are hardware limitations in promotion

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