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Remote Blood Pulse Analysis for Face Presentation Attack Detection

  • Guillaume HeuschEmail author
  • Sébastien Marcel
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

In this chapter, the usage of Remote Photoplethysmography (rPPG) as a mean for face presentation attack detection is investigated. Remote photoplethysmography consists in retrieving the heart-rate of a subject from a video sequence containing some skin, and recorded at a distance. To get a pulse signal, such methods take advantage of subtle color variation on skin pixels due to the blood flowing through vessels. Since the inferred pulse signal gives information on the liveness of the recorded subject, it can be used for biometric presentation attack detection (PAD). Inspired by work made for speaker presentation attack detection, we propose to use long-term spectral statistical features of the pulse signal to discriminate real accesses from attack attempts. A thorough experimental evaluation, with different rPPG and classification algorithms is carried on four publicly available datasets containing a wide range of face presentation attacks. Obtained results suggest that the proposed features are effective for this task, and we empirically show that our approach performs better than state-of-the-art rPPG-based presentation attack detection algorithms.

Notes

Acknowledgements

Part of this research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200005. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland

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