The performance of a biometric system that relies on a single biometric modality (eg,
fingerprints only) is often stymied by various factors such as poor data quality or limited …

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to
improve the generalization on unseen scenarios. Previous methods typically rely on domain …

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 (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 face image is the most accessible biometric modality which is used for highly accurate
face recognition systems, while it is vulnerable to many different types of presentation …

Many prior face anti-spoofing works develop discriminative models for recognizing the
subtle differences between live and spoof faces. Those approaches often regard the image …

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 …

Face anti-spoofing is crucial to security of face recognition systems. Previous approaches
focus on develo** discriminative models based on the features extracted from images …

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have
drawn growing attention due to promising performances for target scenarios. Most existing …

Face anti-spoofing (FAS) is essential for securing face recognition systems. Despite the
decent performance, few existing works fully leverage temporal information. This would …