This work studies the generalization issue of face anti-spoofing (FAS) models on domain
gaps, such as image resolution, blurriness and sensor variations. Most prior works regard …

Abstract Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In
real-world scenarios FRs are confronted with both physical and digital attacks. However …

Face anti-spoofing (FAS) or presentation attack detection is an essential component of face
recognition systems deployed in security-critical applications. Existing FAS methods have …

The availability of handy multi-modal (ie, RGB-D) sensors has brought about a surge of face
anti-spoofing research. However, the current multi-modal face presentation attack detection …

Abstract Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the
model's performance on unseen domains. Existing methods either rely on domain labels to …

Abstract Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems
against presentation attacks. While domain generalization (DG) methods have been …

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face
recognition system is live or fake. Current methods which are trained with existing fake faces …

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various
physical attacks. However, recent research generally focuses on short-distance applications …

Abstract Face Anti-Spoofing (FAS) is recently studied under the continual learning setting,
where the FAS models are expected to evolve after encountering data from new domains …

Recently, vision transformer (ViT) based multimodal learning methods have been proposed
to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no …