Generalizable face anti-spoofing (FAS) based on domain generalization (DG) has gained
growing attention due to its robustness in real-world applications. However, these DG …

Generalizable Face anti-spoofing (FAS) approaches have recently garnered considerable
attention due to their robustness in unseen scenarios. Some recent methods incorporate …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Previous face Presentation Attack Detection (PAD) methods aim to improve the effectiveness
of cross-domain tasks. However, in real-world scenarios, the original training data of the pre …

Recently, vision transformer (ViT) based multimodal learning methods have been proposed
to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no …

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from
presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and …

Abstract Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against
presentation attacks. With advancements in sensor manufacture and multi-modal learning …

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition
system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on …

Many face anti-spoofing (FAS) methods have focused on learning discriminative features
from both live and spoof training data to strengthen the security of face recognition systems …

In real-world scenarios, extensive manual annotation for continual learning is impractical
due to prohibitive costs. Although prior arts, influenced by large-scale webly supervised …