Dual Consistency Regularization for Generalized Face Anti-Spoofing

Abstract

Recent Face Anti-Spoofing (FAS) methods have improved generalization to unseen domains by leveraging domain generalization techniques. However, they overlooked the semantic relationships between local features, resulting in suboptimal feature alignment and limited performance. To this end, pixel-wise supervision has been introduced to offer contextual guidance for better feature alignment. Unfortunately, the semantic ambiguity in coarsely designed pixel-wise supervision often leads to misalignment. This paper proposes a novel Dual Consistency Regularization Network (DCRN). It promotes the fine-grained alignment of local features with dense semantic correspondence for FAS. Specifically, a Dual Consistency Learning module (DCL) is devised to capture the inter- and intra-similarity between each region of sample pairs. In this module, a dual consistency regularization learning objective enhances the semantic consistency of local features by minimizing both the variance of inter-similarity and the distance between inter- and intra-similarity. Further, a weight matrix is estimated based on the inter-similarity, representing the possibility that each region belongs to the living class. Based on this weight matrix, WMSE loss is designed to guide the model in avoiding mapping the live regions to the spoofing class, thus alleviating semantic ambiguity in pixel-wise supervision. Extensive experiments on four widely used datasets clearly demonstrate the superiority and high generalization of the proposed DCRN.