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Towards Good Practices in Face Anti-Spoofing: An Image Reconstruction Based Method

Yang Chen 1 Tian Wang 2 Jingjing Wang 3 Peng Shi 4 Hichem Snoussi 5
1 Department of Mathematics
2 School of Automation and Electrical Engineering [Beijing] (University of Science and Technology Beijing)
3 Key Laboratory of Resource Biology and Biotechnology
4 Fujian Normal University [Fujian]
5 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
ICD - Institut Charles Delaunay
Abstract : Face anti-spoofing is vital to the security of face recognition. Previous methods rely on hand-crafted features or use estimate depth map as supervision in CNNs and result in poor performance. In this paper, we propose a novel auto-encoder image reconstruction model to address the face anti-spoofing problem. In our model, we introduce the multi-scale skip connections to excavate multi-level semantics contained in images. Further, we combine the SSIM loss and Ll loss to guide the network towards better reconstruction results. We verify the effectiveness of our model design on OULU-NPU and SiW datasets and obtain promising results.
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https://hal-utt.archives-ouvertes.fr/hal-02486756
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Friday, February 21, 2020 - 10:52:34 AM
Last modification on : Wednesday, June 9, 2021 - 2:41:39 PM

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Yang Chen, Tian Wang, Jingjing Wang, Peng Shi, Hichem Snoussi. Towards Good Practices in Face Anti-Spoofing: An Image Reconstruction Based Method. 2019 Chinese Automation Congress (CAC), Nov 2019, Hangzhou, China. pp.4700-4705, ⟨10.1109/CAC48633.2019.8997504⟩. ⟨hal-02486756⟩

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