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1 August 2023 Adaptive and learnable label for face anti-spoofing
Shijie Chen, Yifeng Wang, Yuehu Han
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Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543A (2023) https://doi.org/10.1117/12.2684535
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China
Abstract
Face anti-spoofing(FAS) is an important task to secure face recognition systems from presentation attacks (PAs). Most of the FAS methods use scalar(0,1) or handcrafted pixel-wise labels(binary mask) as the ground truth labels. However, these labels may are not the most adequate way to supervise PA detectors learning intrinsic and discriminative spoofing cues. In this work, we present an adaptive and learnable label for more efficient supervision of PA detectors. Then we propose a dual-branch network for FAS that utilizes the proposed supervision to detect PAs more reasonably. We evaluate our method by conducting experiments on public datasets, the proposed supervision method can not only improve the detection performance of the PA detectors, but also enhance the model’s interpretability (i.e., locating the pixel-level positions of PAs more reasonably).
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Shijie Chen, Yifeng Wang, and Yuehu Han "Adaptive and learnable label for face anti-spoofing", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543A (1 August 2023); https://doi.org/10.1117/12.2684535
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KEYWORDS
Facial recognition systems
Computer vision technology
Deep learning
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