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Learning Generalized Spoof Cues for Face Anti-spoofing
[article]
2020
arXiv
pre-print
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
arXiv:2005.03922v1
fatcat:tsqoi5rqzrcarlhdw7ss7a6qyy