Open Set Face Anti-Spoofing in Unseen Attacks

In this paper, we propose an end-to-end open set face anti-spoofing (OSFA) approach for unseen attack recognition. Previous domain generalization approaches aim to align multiple domains beyond one common subspace, leading to performance degradation due to the discrepancy of different domains. To address this issue, our approach formulates face anti-spoofing (FAS) in an open set recognition framework, which learns compact representation for each known class in parallel to recognizing unseen attack examples. To this end, we introduce the statistical extreme value theory incorporated in our objective under the multi-task framework. Moreover, we develop an identity-aware contrastive learning method, preventing us from confusion in unseen attack examples versus hard examples. Experimental results on four datasets demonstrate the robustness of our proposed OSFA, especially under diverse categories of unseen attacks.