CDNet: Cross-frequency Dual-branch Network for Face Anti-Spoofing

Xiaobin Huang, Qiufu Li, Linlin Shen, Xingwei Chen

Abstract: Face anti-spoofing (FAS) defends the facial image recognition systems against the spoof attacks. While the imperceptible spoof cues in the facial images are usually represented in the images' high-frequency components, existing methods do not fully explore them. In this paper, we introduce wavelet into face anti-spoofing and propose a Cross-frequency Dual-branch network (CDNet), which mainly contains two frequency branches to explore spoof cues from the input facial images' high- and low-frequency components generated by wavelet transforms. In CDNet, we design Frequency Attention Module (FAM) to fuse different internal frequency features learned by two frequency branches, and propose a Complementary Learning Module (CLM) to aggregate the two final frequency features. In addition, we present a resolution-aware Binary Cross-Entropy Loss to balance the training samples with different resolutions. We conduct comprehensive experiments on four datasets, and the results shows that our CDNet performs better than the previous state-of-the-art methods on both intra- and inter-dataset testing.