Learning Inter and Intra Class Variation With Deep Frequency Factorization Network for Face Anti-Spoofing

In this paper, we aim to enhance the generalization ability of single-shot face anti-spoofing tasks in two aspects: 1) we establish a deep frequency factorization network (DFF-Net) to capture deep frequency information, enabling better discrimination of multiple styles of face spoofing, including both 2D and 3D methods; 2) aided by a sample redistribution strategy, we construct a unified anomaly metric-based supervision system to address unknown face attacks. Specifically, the proposed DFF-Net explicitly extracts spoofing cues from the high, low, and fusion frequency domains by embedding a deep frequency filter module into each frequency branch. Furthermore, the devised sample redistribution strategy enables the reorganization of learned features, making them more suitable for the three types of anomaly metric-based supervisions that correspond to the three frequency branches. This approach facilitates iterative learning of the importance of spoofing cues. Experimental results demonstrate that our proposed framework achieves state-of-the-art performance on both monocular and multi-spectral benchmark datasets.