Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the
presentation attacks (PAs). As more and more realistic PAs with novel types spring up, it is …

The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

With diverse presentation attacks emerging continually, generalizable face anti-spoofing
(FAS) has drawn growing attention. Most existing methods implement domain generalization …

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 …

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in
securing face recognition systems from presentation attacks (PAs). As more and more …

Face anti-spoofing (FAS) plays a critical role in securing face recognition systems from
different presentation attacks. Previous works leverage auxiliary pixel-level supervision and …

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing
methods heavily rely on the expert-designed networks, which may lead to a sub-optimal …

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 …

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 …