Multi-supervision face in-vivo detection method fusing multi-scale features

Abstract

The invention discloses a multi-scale feature fused multi-supervision face in-vivo detection method, which comprises the following steps of: acquiring an image data set, and preprocessing the data set; gradient texture features are extracted through central difference convolution, and codes are fused; extracting multi-scale discriminant features through a group receptive field branch, and splicing and fusing the multi-scale discriminant features with a gradient texture branch; fusing the two features, inputting the fused features into a residual structure, carrying out deep semantic learning and coding, and inputting a result into a depth map generator and a mask generator to obtain a feature map; a depth map is used for supervision, and a binary mask is used for auxiliary supervision; and fusing output results of the depth map generator and the mask generator, calculating a prediction score, and realizing end-to-end living body detection. According to the invention, the performance and generalization ability of the network can be improved, and the method has the advantages of small parameter quantity and end-to-end detection; compared with an existing mainstream living body detection algorithm, the method is higher in detection precision and better in robustness.

Description

technical field [0001] The present invention relates to the technical field of face liveness detection, in particular to a multi-supervised face liveness detection method integrating multi-scale features. Background technique [0002] In recent years, face recognition systems have been widely used in transportation, surveillance and other fields due to their low cost and easy installation. However, the face recognition system also has certain loopholes. Attackers use the face information of legitimate users to attack the system, which causes great harm to the rights and interests of users. Commonly used fraudulent attack methods include photo, video and 3D Mask attacks. In order to solve this problem, more and more researchers have begun to pay attention to face detection technology. [0003] Face liveness detection is a technology that recognizes that the face in front of the camera is a real face and is a fraudulent face processed by devices such as photos or electronic s...

AI Technical Summary

Problems solved by technology

However, most of the current deep learning algorithms focus on the optimization of neural network models, while ignoring the effectiveness of traditional feature description operators in feature extraction. Existing living body detection models generally have a huge amount of calculation. When extracting timing information, better equipment is often required, and the time complexity of detecting living bodies also increases accordingly.

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