Deep features extracted from different domains have shown great advantages in the face
anti-spoofing task. Previous extraction strategies consider less of the extent variation in …

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Recently,
central difference convolution (CDC) has shown its excellent representation capacity for the …

Currently, most face anti-spoofing methods target the generalization problem by relying on
auxiliary information such as additional annotations and modalities. However, this auxiliary …

Although significant progress has been made in face anti-spoofing, current methods can
only achieve satisfactory results under intra-dataset settings. In other words, they tend to …

Face anti-spoofing detection is critical to guarantee the security of biometric face recognition
systems. Despite extensive advances in facial anti-spoofing based on single-model image …

Face Anti-spoofing gains increased attentions recently in both academic and industrial
fields. With the emergence of various CNN based solutions, the multi-modal (RGB, depth …

One primary challenge in face anti-spoofing refers to suffering a sharp performance drop in
cross-domain scenes, where training and testing images are collected from different …

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops,
and so on, face anti-spoofing has risen to significance and is an active research area in …

Face anti-spoofing aims to counter facial presentation attacks and heavily relies on
identifying live/spoof discriminative features. In this paper, we propose a novel Learnable …

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 …