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

There has been an increasing consensus in learning based face anti-spoofing that the
divergence in terms of camera models is causing a large domain gap in real application …

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 an important task in full-stack face applications including face
detection, verification, and recognition. Previous approaches build models on datasets …

Since the human face preserves the richest information for recognizing individuals, face
recognition has been widely investigated and achieved great success in various …

Inspired by the philosophy employed by human beings to determine whether a presented
face example is genuine or not, ie, to glance at the example globally first and then carefully …

Face anti-spoofing (aka presentation attack detection) has recently emerged as an active
topic with great significance for both academia and industry due to the rapidly increasing …

Face anti-spoofing is critical to the security of face recognition systems. Depth supervised
learning has been proven as one of the most effective methods for face anti-spoofing …

Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been
extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task …

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