Multi-channel face liveness detection based on multi-scale feature fusion

Abstract

A multi-channel face liveness detection method based on multi-scale feature fusion is proposed to solve the problems of poor stability, poor generalization, and poor robustness against unknown attacks of existing face liveness detection models. Firstly, the method uses a multichannel residual network and introduces the center differential convolution and SimAM attention module in the residual block to improve the feature extraction ability and stability of the model. Secondly, the information contained in the feature map at different scales is further mined by multiscale feature fusion at the end of each channel. Finally, the network is supervised by using cross modal focal loss as an aid to binary cross entropy loss. Extensive evaluations in two publicly available datasets demonstrate the effectiveness, generalization, and robustness of the proposed method against unknown attacks.