Generating synthetic datasets for training face recognition models is challenging because
dataset generation entails more than creating high fidelity images. It involves generating …

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 …

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 Presentation Attack Detection (PAD) approaches based on multi-modal data have
been attracted increasingly by the research community. However, they require multi-modal …

Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed
“spoof trace", eg, color distortion, 3D mask edge, Moiré pattern, and many others. Designing …

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

Prior studies show that the key to face anti-spoofing lies in the subtle image patterns, termed
“spoof trace,” eg, color distortion, 3D mask edge, and Moiré pattern. Spoof detection rooted …

In this work, we study multi-domain learning for face anti-spoofing (MD-FAS), where a pre-
trained FAS model needs to be updated to perform equally well on both source and target …

Although existing face anti-spoofing (FAS) methods achieve high accuracy in intra-domain
experiments, their effects drop severely in cross-domain scenarios because of poor …