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Understanding deep face anti-spoofing: from the perspective of data

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Abstract

Face biometrics systems are increasingly used by many business applications, which can be vulnerable to malicious attacks, leading to serious consequences. How to effectively detect spoofing faces is a critical problem. Traditional methods rely on handcraft features to distinguish real faces from fraud ones, but it is difficult for feature descriptors to handle all attack variations. More recently, in order to overcome the limitation of traditional methods, newly emerging CNN-based approaches were proposed, most of which, if not all, carefully design different network architectures. To make CNN-related approaches effective, data and learning strategies are both indispensable. In this paper, instead of focusing on network design, we explore more from the perspective of data. We present that appropriate nonlinear adjustment and hair geometry can amplify the contrast between real faces and attacks. Given our exploration, a simple convolutional neural network can solve the face anti-spoofing problem under different attack scenarios and achieve state-of-the-art performance on well-known face anti-spoofing benchmarks.

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Author notes

  1. Yujing Sun and Hao Xiong have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong, China

    Yujing Sun, Hao Xiong & Siu Ming Yiu

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  1. Yujing Sun
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  2. Hao Xiong
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  3. Siu Ming Yiu
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Correspondence to Yujing Sun.

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Prof. Xiu Ming Yiu has received research Grants from the Hong Kong Government.

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This project is partially supported by a Collaborative Research Fund (CRF, C1008-16G) and Innovation and Technology Support Programme (ITS/173/18FP) of the Hong Kong Government.

Appendix

Appendix

Fig. 14
figure 14

Nonlinear contrast and brightness adjustment for type Face. The x axis (\(\alpha \), \(\beta \)) indicates the amount of adjustment (%) and the y axis shows the HTER error (\(\%\)). The blue line indicates the error produced by the original data for all image resolutions while the red curve indicates the errors for all image resolutions at every adjustment

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Fig. 15
figure 15

Linear contrast and brightness adjustment. The blue line indicates the error produced by the original data for all image resolutions while the red curve indicates the errors for all image resolutions at every adjustment

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Fig. 16
figure 16

Visualization of hair feature maps in paper attacks and curved paper attacks. For the ones with adjustment, the adjustment is + 35% nonlinear improvement on both contrast and brightness

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See Figures 14, 15 and 16.

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Sun, Y., Xiong, H. & Yiu, S.M. Understanding deep face anti-spoofing: from the perspective of data. Vis Comput 37, 1015–1028 (2021). https://doi.org/10.1007/s00371-020-01849-x

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