Disentangled Representation with Dual-stage Feature Learning for Face
Anti-spoofing
- URL: http://arxiv.org/abs/2110.09157v1
- Date: Mon, 18 Oct 2021 10:22:52 GMT
- Title: Disentangled Representation with Dual-stage Feature Learning for Face
Anti-spoofing
- Authors: Yu-Chun Wang, Chien-Yi Wang, Shang-Hong Lai
- Abstract summary: It is essential to learn more generalized and discriminative features to prevent overfitting to pre-defined spoof attack types.
This paper proposes a novel dual-stage disentangled representation learning method that can efficiently untangle spoof-related features from irrelevant ones.
- Score: 18.545438302664756
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: As face recognition is widely used in diverse security-critical applications,
the study of face anti-spoofing (FAS) has attracted more and more attention.
Several FAS methods have achieved promising performances if the attack types in
the testing data are the same as training data, while the performance
significantly degrades for unseen attack types. It is essential to learn more
generalized and discriminative features to prevent overfitting to pre-defined
spoof attack types. This paper proposes a novel dual-stage disentangled
representation learning method that can efficiently untangle spoof-related
features from irrelevant ones. Unlike previous FAS disentanglement works with
one-stage architecture, we found that the dual-stage training design can
improve the training stability and effectively encode the features to detect
unseen attack types. Our experiments show that the proposed method provides
superior accuracy than the state-of-the-art methods on several cross-type FAS
benchmarks.
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