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

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 …

A face mask worn by a single person or a group of individuals can be identified using face
mask detection, an AI-based system that examines a video stream. A confidence value is …

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 …

Biometrics has been evolving as an exciting yet challenging area in the last decade. Though
face recognition is one of the most promising biometrics techniques, it is vulnerable to …

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, significant progress has been made in face presentation attack detection (PAD),
which aims to secure face recognition systems against presentation attacks, owing to the …

Big data analytics, computer vision, and human-level governance are key areas where deep
learning has been impactful. However, its advancements have also led to concerns over …