Learning Generalized Spoof Cues for Face Anti-spoofing

Understand

Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries for some predefined spoof types.

• However, the diversity of the spoof samples including the unknown ones hinders the effective decision boundary modeling and leads to weak generalization capability.
• In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues.
• The proposed framework consists of a spoof cue generator and an auxiliary classifier.