Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

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 …

Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art
FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak …

Detecting manipulated facial images and videos is an increasingly important topic in digital
media forensics. As advanced face synthesis and manipulation methods are made …

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 vital role in securing face recognition systems. Existing
methods heavily rely on the expert-designed networks, which may lead to a sub-optimal …

Existing domain generalization methods for face anti-spoofing endeavor to extract common
differentiation features to improve the generalization. However, due to large distribution …

As facial interaction systems are prevalently deployed, security and reliability of these
systems become a critical issue, with substantial research efforts devoted. Among them, face …

Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from
presentation attacks. Most existing FAS methods capture various cues (eg, texture, depth …