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Progressive Transfer Learning for Face Anti-Spoofing
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DOI：10.1109/TIP.2021.3066912.png)
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Face anti-spoofing (FAS) techniques play an important role in defending face recognition systems against spoofing attacks. Existing FAS methods often require a large number of annotated spoofing face data to train effective anti-spoofing models. Considering the attacking nature of spoofing data and its diverse variants, obtaining all the spoofing types in advance is difficult. This would limit the performance of FAS networks in practice. Thus, an online learning FAS method is highly desirable. In this paper, we present a semi-supervised learning based framework to tackle face spoofing attacks with only a few labeled training data (e.g., similar to 50 face images). Specifically, we progressively adopt the unlabeled data with reliable pseudo labels during training to enrich the variety of training data. We observed that face spoofing data are naturally presented in the format of video streams. Thus, we exploit the temporal consistency to consolidate the reliability of a pseudo label for a selected image. Furthermore, we propose an adaptive transfer mechanism to ameliorate the influence of unseen spoofing data. Benefiting from the progressively-labeling nature of our method, we are able to train our network on not only data of seen spoofing types (i.e., the source domain) but also unlabeled data of unseen attacking types (i.e., the target domain). In this way, our method can reduce the domain gap and is more practical in real-world anti-spoofing scenarios. Extensive experiments in both the intra-database and inter-database scenarios demonstrate that our method is on par with the state-of-the-art methods but employs remarkably less labeled data (less than 0.1% labeled spoofing data in a dataset). Moreover, our method significantly outperforms fully-supervised methods on cross-domain testing scenarios with the help of our progressive learning fashion.
Keyword:
Face anti spoofing
progressive learning
transfer learning
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13.7
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1.0W
被引数:
8.4W
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