University of Oulu

Xiaobai Li, J. Komulainen, G. Zhao, Pong-Chi Yuen and M. Pietikäinen, "Generalized face anti-spoofing by detecting pulse from face videos," 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, 2016, pp. 4244-4249. doi: 10.1109/ICPR.2016.7900300

Generalized face anti-spoofing by detecting pulse from face videos

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Author: Li, Xiaobai1; Komulainen, Jukka1; Zhao, Guoying1;
Organizations: 1CMVS, University of Oulu, Oulu, Finland
2Department of Computer Science, HKBU, Hong Kong
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-02-25


Face biometric systems are vulnerable to spoofing attacks. Such attacks can be performed in many ways, including presenting a falsified image, video or 3D mask of a valid user. A widely used approach for differentiating genuine faces from fake ones has been to capture their inherent differences in (2D or 3D) texture using local descriptors. One limitation of these methods is that they may fail if an unseen attack type, e.g. a highly realistic 3D mask which resembles real skin texture, is used in spoofing. Here we propose a robust anti-spoofing method by detecting pulse from face videos. Based on the fact that a pulse signal exists in a real living face but not in any mask or print material, the method could be a generalized solution for face liveness detection. The proposed method is evaluated first on a 3D mask spoofing database 3DMAD to demonstrate its effectiveness in detecting 3D mask attacks. More importantly, our cross-database experiment with high quality REAL-F masks shows that the pulse based method is able to detect even the previously unseen mask type whereas texture based methods fail to generalize beyond the development data. Finally, we propose a robust cascade system combining two complementary attack-specific spoof detectors, i.e. utilize pulse detection against print attacks and color texture analysis against video attacks.

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ISBN Print: 978-1-5090-4847-2
Pages: 4244 - 4249
DOI: 10.1109/ICPR.2016.7900300
Host publication: 2016 Proceedings of 23rd International conference on Pattern Recognition (ICPR 2016)
Conference: International Conference on Pattern Recognition
Type of Publication: B3 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
319 Forensic science and other medical sciences
Funding: This work was sponsored by the Academy of Finland, Infotech Oulu and Tekes Fidipro program. This work was also partially supported by Hong Kong RGC General Research Fund HKBU 12201215.
Copyright information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.