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 …

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

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) 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 …

Face anti-spoofing is crucial to prevent face recognition systems from a security breach.
Previous deep learning approaches formulate face anti-spoofing as a binary classification …

Automatic face recognition is now widely used in applications ranging from deduplication of
identity to authentication of mobile payment. This popularity of face recognition has raised …

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 …

Biometric recognition refers to the automated recognition of individuals based on their
biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first …

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 …

Spoofing attacks are one of the security traits that biometric recognition systems are proven
to be vulnerable to. When spoofed, a biometric recognition system is bypassed by …