Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space

Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frame-works via directly aligning diverse source samples into a common feature space. However, these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues, we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR)framework to learn embedding in hyperbolic space, which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail, we propose the hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover, we design a Prototype-oriented Classifier, which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors.