Feature Generation and Hypothesis Verification for Reliable Face Anti-spoofing
نویسندگان
چکیده
Although existing face anti-spoofing (FAS) methods achieve high accuracy in intra-domain experiments, their effects drop severely cross-domain scenarios because of poor generalization. Recently, multifarious techniques have been explored, such as domain generalization and representation disentanglement. However, the improvement is still limited by two issues: 1) It difficult to perfectly map all faces a shared feature space. If from unknown domains are not mapped known region space, accidentally inaccurate predictions will be obtained. 2) hard completely consider various spoof traces for In this paper, we propose Feature Generation Hypothesis Verification framework alleviate issues. Above all, generation networks which generate hypotheses real attacks introduced first time FAS task. Subsequently, hypothesis verification modules applied judge whether input comes real-face space distribution respectively. Furthermore, some analyses relationship between our Bayesian uncertainty estimation given, provides theoretical support reliable defense domains. Experimental results show achieves promising outperforms state-of-the-art approaches on extensive public datasets.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2022
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v36i2.20071