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 …

Although current face anti-spoofing methods achieve promising results under intra-dataset
testing, they suffer from poor generalization to unseen attacks. Most existing works adopt …

Face anti-spoofing (FAS) techniques play an important role in defending face recognition
systems against spoofing attacks. Existing FAS methods often require a large number of …

Nowadays, the increasingly growing number of mobile and computing devices has led to a
demand for safer user authentication systems. Face anti-spoofing is a measure towards this …

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have
drawn growing attention due to promising performances for target scenarios. Most existing …

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

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 …

Due to the environmental differences, many face anti-spoofing methods fail to generalize to
unseen scenarios. In light of this, we propose a unified unsupervised and semi-supervised …

Despite promising success in intra-dataset tests, existing face anti-spoofing (FAS) methods
suffer from poor generalization ability under domain shift. This problem can be solved by …

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 …