Existing face anti-spoofing models using deep learning for multimodality data suffer from low
generalization in the case of using variety of presentation attacks, such as 2-D printing and …

Local features contain crucial clues for face anti-spoofing. Convolutional neural networks
(CNNs) are powerful in extracting local features, but the intrinsic inductive bias of CNNs …

Face anti-spoofing is essential to prevent face recognition systems from a security breach.
Much of the progresses have been made by the availability of face anti-spoofing benchmark …

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 …

Effectively identifying attacked faces is an urgent problem to be solved in the scenario of
face recognition. As deep learning is applied to face anti-spoofing (FAS), many multi-modal …

Since the rapid development of face recognition systems using 3D cameras, the public has
demanded great safety regulations for these devices. As a closely related topic, multimodal …

Regardless of the usage of deep learning and handcrafted methods, the dynamic
information from videos and the effect of cross-ethnicity are rarely considered in face anti …

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

In this paper, we propose a novel framework leveraging the advantages of the
representational ability of deep learning and domain generalization for face spoofing …

Multimodal face anti-spoofing systems adopt multiple sensor modalities, such as infrared,
color, depth, and thermal, to distinguish between living and spoofing faces via …