Learning Generalized Spoof Cues for Face Anti-spoofing
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Haocheng Feng and Zhibin Hong and Haixiao Yue and Yang Chen and Keyao Wang and Junyu Han and Jingtuo Liu and Errui Ding
2020
Abstract
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. The generator minimizes the spoof
cues of live samples while imposes no explicit constraint on those of spoof
samples to generalize well to unseen attacks. In this way, anomaly detection is
implicitly used to guide spoof cue generation, leading to discriminative
feature learning. The auxiliary classifier serves as a spoof cue amplifier and
makes the spoof cues more discriminative. We conduct extensive experiments and
the experimental results show the proposed method consistently outperforms the
state-of-the-art methods. The code will be publicly available at
https://github.com/vis-var/lgsc-for-fas.
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