The Shallow Network with Learnable Attention Module For Face Anti-spoofing
Authors: 
Yifeng Ren, Guigui Yun, Qingyan Li, Zhe LiuAuthors Info & Claims
Pages 224 - 228
Abstract
With the development of face recognition systems in mobile devices and intelligent systems, the vulnerability of face recognition techniques have been widely focused as it will introduce tremendous risks for these systems. So the face anti-spoofing approaches are developed to avoid the influence of fake faces by printed or replayed ways. The existing methods are devoted to improve the performance of face anti-spoofing while the runtime of detecting fake faces in a fast speed is also important in real-world applications. Therefore, in this paper, we propose a shallow network to speed up the runtime of deep model while reserving the performance of predicting fake faces. In the shallow network, one convolution layer and two recurrent convolution layer are used to extract the visual features for the fake faces. Then a spatial-channel attention layer is used to follow the important regions and feature maps in the network. The most challenging dataset, namely SiW dataset, is used to evaluate our proposed method and the results are proven the efficiency and effectiveness of the proposed method.
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Published In
May 2021
293 pages
ISBN:9781450389655
DOI:10.1145/3473258
Copyright © 2021 ACM.
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Association for Computing Machinery
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Published: 11 December 2021
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ICBBT '21
ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology
May 21 - 23, 2021
Xi'an, China
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