Face deception detection method based on domain adaptive learning and domain generalization

Abstract

The invention discloses a face cheating detection method based on domain adaptive learning and domain generalization. The face cheating detection method mainly comprises the following steps: constructing an encoder based on a deep residual network; constructing a classifier for detecting face cheating; constructing a discriminator which is used for guiding characteristics to accord with Laplace distribution; forming a training network by using the three parts; constructing a loss function of network training; setting a model optimization algorithm; processing the training data set sample imageto change the size; training and optimizing network parameters; processing the test image to change the size; and carrying out face cheating detection by using the trained encoder and classifier. According to the invention, common features of source domain training data are extracted through a maximum mean difference MMD training encoder; and meanwhile, by combining AAE technology of an anti-autoencoder. The characteristics conform to Laplace distribution, the generalization performance of the detection method is further improved, and the detection performance of the method on face spoofing attacks under complex conditions in practical application is effectively improved.

Description

technical field [0001] The invention relates to the technical fields of computer vision and artificial intelligence, in particular to a face deception detection method based on domain adaptive learning and domain generalization. Background technique [0002] Face recognition has always been a hot research direction in the field of computer vision. Due to the non-intrusive and interactive nature of face recognition, its application in user identity authentication is becoming more and more extensive. At the same time, with the popularity of the Internet and various smart devices, the frequency of face recognition systems being spoofed and attacked is becoming more and more frequent, and the attack methods are becoming more and more diverse. Therefore, improving the deception detection ability of the face recognition system can effectively enhance the security of the system, which has important research significance and urgent practical needs. [0003] Face spoofing detection...

AI Technical Summary

Problems solved by technology

This method effectively overcomes the shortcoming of insufficient generalization ability of the existing technology, enhances the cross-database detection ability of the face spoofing detection system, and improves its practicability

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