Generalized Face Antispoofing by Learning to Fuse Features From High- and Low-Frequency Domains

Abstract

In this article, we propose a face spoofing detection method by learning to fuse high-frequency (HF) and low-frequency (LF) features, in an effort to improve the generalization capability and fill up the domain gap between training and testing when the antispoofing is practically conducted in unseen scenarios. In particular, the proposed face antispoofing model consists of two streams that extract HF and LF components of a facial image with three high-pass and three low-pass filters. Moreover, considering the fact that spoofing features exist in different feature levels, we train our network with a novel multiscale triplet loss. The cross-frequency spatial attention module further enables the two streams to communicate and exchange information with each other. Finally, the outputs of the two streams are fused with a weighting strategy for final classification. Extensive experiments conducted on intra- and cross-database settings show the superiority of the proposed scheme.