With various face presentation attacks arising under unseen scenarios, face anti-spoofing
(FAS) based on domain generalization (DG) has drawn growing attention due to its …

Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries
for some predefined spoof types. However, the diversity of the spoof samples including the …

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 …

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

Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to
their robustness for diverse presentation attacks in unseen scenarios. Most previous …

Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
Empirically, given an image, a model with more consistent output on different views (ie …

Face anti-spoofing aims to detect presentation attack to face recognition--based
authentication systems. It has drawn growing attention due to the high security demand. The …

Face anti-spoofing is significant to the security of face recognition systems. Previous works
on depth supervised learning have proved the effectiveness for face anti-spoofing …

While the performance of face recognition systems has improved significantly in the last
decade, they are proved to be highly vulnerable to presentation attacks (spoofing). Most of …

Existing face anti-spoofing (FAS) methods fail to generalize well to unseen domains with
different data distribution from the training domains, due to the distribution discrepancies …