Face Anti-spoofing: Multi-spectral Approach

  • Dong YiEmail author
  • Zhen Lei
  • Zhiwei Zhang
  • Stan Z. Li
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


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.


Face Recognition Face Image Gabor Feature Active Shape Model Face Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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, and AuthenMetric R&D Funds.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Chinese Academy of SciencesInstitute of AutomationBeijingChina

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