Generalized Face Liveness Detection via De-spoofing Face Generator
Xingming Long Shiguang Shan Jie Zhang
Previous Face Anti-spoofing (FAS) works face the challenge of generalizing in
unseen domains. One of the major problems is that most existing FAS datasets
are relatively small and lack data diversity. However, we find that there are
numerous real faces that can be easily achieved under various conditions, which
are neglected by previous FAS works. In this paper, we conduct an Anomalous cue
Guided FAS (AG-FAS) method, which leverages real faces for improving model
generalization via a De-spoofing Face Generator (DFG). Specifically, the DFG
trained only on the real faces gains the knowledge of what a real face should
be like and can generate a "real" version of the face corresponding to any
given input face. The difference between the generated "real" face and the
input face can provide an anomalous cue for the downstream FAS task. We then
propose an Anomalous cue Guided FAS feature extraction Network (AG-Net) to
further improve the FAS feature generalization via a cross-attention
transformer. Extensive experiments on a total of nine public datasets show our
method achieves state-of-the-art results under cross-domain evaluations with
unseen scenarios and unknown presentation attacks.