Face Anti-Spoofing by Fusing High and Low Frequency Features for Advanced Generalization Capability

In face authentication systems, face anti-spoofing is an indispensable part. Recently, CNN-based approaches have achieved promising results when training and testing in similar scenes. However, performance usually drops drastically when the model is tested on unseen datasets due to the domain generalization problem. In this paper, we propose a new face anti-spoofing model consisting of two streams to fuse high frequency (HF) and low frequency (LF) information of a facial image for high generalization capability. More concretely, three high-pass and low-pass filters are utilized to extract high and low frequency component of a facial image, respectively. The two components are proceeded by two sub-networks with a cross-frequency spatial attention (CFSA) module, which makes two streams communicate and exchange information with each other. Considering the two sub-networks are responsible for different kinds of information, self-channel attention is incorporated after CFSA, then the outputs of the two sub-networks are fused for final classification. Experiments on cross-database results show that the proposed method can largely improve the generalization capacity in face spoofing detection.