Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing
This addresses the challenge of generalizing face anti-spoofing models across different image domains without prior knowledge of attack types, which is incremental as it builds on existing methods.
The paper tackles the problem of domain-generalized face anti-spoofing by proposing a deep learning model that disentangles facial liveness representation from irrelevant features, achieving favorable performance against state-of-the-art approaches in identifying novel spoof attacks in unseen image domains.
Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated classification task. In practice, one would expect such models to be generalized to FAS in different image domains. Moreover, it is not practical to assume that the type of spoof attacks would be known in advance. In this paper, we propose a deep learning model for addressing the aforementioned domain-generalized face anti-spoofing task. In particular, our proposed network is able to disentangle facial liveness representation from the irrelevant ones (i.e., facial content and image domain features). The resulting liveness representation exhibits sufficient domain invariant properties, and thus it can be applied for performing domain-generalized FAS. In our experiments, we conduct experiments on five benchmark datasets with various settings, and we verify that our model performs favorably against state-of-the-art approaches in identifying novel types of spoof attacks in unseen image domains.