CSDG-FAS: Closed-Space Domain Generalization for Face Anti-spoofing

Keyao Wang, Guosheng Zhang, Haixiao Yue, Yanyan Liang, Mouxiao Huang, Gang Zhang, Junyu Han, Errui Ding, Jingdong Wang

Abstract: Domain generalization based Face Anti-spoofing (FAS) aims to enhance its ability to work in unseen domains. Existing methods endeavor to extract a discriminative common space through the alignment of distribution in each domain. However, he inherent diversity within spoof faces significantly challenges the establishment of such a unified space. In this work, we reframe domain generalization-based FAS as an anomaly detection problem, positing that real faces tend to aggregate within a compact, closed space, whereas spoof faces exhibit a preference for dispersion within an open space. Specifically, we introduce a novel Closed Space Domain Generalization (CSDG) framework, consisting of a novel designed Dynamic Feature Queue and a Domain Alignment Module. The former is dedicated to maintaining a distinct class center for real faces, achieved by continuously widening its separation from the dynamically evolving spoof face queue; The latter aims to further align the distribution of real faces across diverse domains. Moreover, we propose a Progressive Training Strategy to effectively mine challenging samples across multiple domains during the training phase. Furthermore, we highlight the success of our proposed methods by achieving the first prize in the Surveillance Face Anti-Spoofing track at Challenge@CVPR 2023. Subsequently, we demonstrate the efficacy of the CSDG framework on two intra-domain datasets, as well as in two challenging cross-domain FAS experiments.