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Handbook of Biometric Anti-Spoofing pp 83–102Cite as

Face Anti-spoofing: Multi-spectral Approach

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

With the wide applications of face recognition, spoofing attack is becoming a big threat to their security. Conventional face recognition systems usually adopt behavioral challenge-response or texture analysis methods to resist spoofing attacks, however, these methods require high user cooperation and are sensitive to the imaging quality and environments. In this chapter, we present a multi-spectral face recognition system working in VIS (Visible) and NIR (Near Infrared) spectrums, which is robust to various spoofing attacks and user cooperation free. First, we introduce the structure of the system from several aspects including: imaging device, face landmarking, feature extraction, matching, VIS, and NIR sub-systems. Then the performance of the multi-spectral system and each subsystem is evaluated and analyzed. Finally, we describe the multi-spectral image-based anti-spoofing module, and report its performance under photo attacks. Experiments on a spoofing database show the excellent performance of the proposed system both in recognition rate and anti-spoofing ability. Compared with conventional VIS face recognition system, the multi-spectral system has two advantages: (1) By combining the VIS and NIR spectrums, the system can resist VIS photo and NIR photo attacks easily. And users’ cooperation is no longer needed, making the system user friendly and fast. (2) Due to the precise key-point localization, Gabor feature extraction and unsupervised learning, the system is robust to pose, illumination and expression variations. Generally, its recognition rate is higher than the VIS subsystem.

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Notes

  1. 1.

    Mask is also a good choice, but usually it is too expensive to produce client-like masks. So the massive usage of masks rarely appears in the literature.

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Acknowledgments

This work was supported by the Chinese National Natural Science Foundation Project #61070146, #61105023, #61103156, #61105037, National IoT R&D Project #2150510, European Union FP7 Project #257289 (TABULA RASA http://www.tabularasa-euproject.org), and AuthenMetric R&D Funds.

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Authors and Affiliations

  1. Chinese Academy of Sciences, Institute of Automation, 95 Zhongguancun Donglu, Beijing, 100190, China

    Dong Yi, Zhen Lei, Zhiwei Zhang & Stan Z. Li

Authors
  1. Dong Yi
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  2. Zhen Lei
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  3. Zhiwei Zhang
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  4. Stan Z. Li
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Corresponding author

Correspondence to Dong Yi .

Editor information

Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, United Kingdom

    Mark S. Nixon

  3. Institute of Automation Center for Biometrics and Security Resea, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

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© 2014 Springer-Verlag London

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Yi, D., Lei, Z., Zhang, Z., Li, S.Z. (2014). Face Anti-spoofing: Multi-spectral Approach. In: Marcel, S., Nixon, M., Li, S. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_5

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  • DOI: https://doi.org/10.1007/978-1-4471-6524-8_5

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  • Print ISBN: 978-1-4471-6523-1

  • Online ISBN: 978-1-4471-6524-8

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