Remote Blood Pulse Analysis for Face Presentation Attack Detection

In this chapter, the usage of Remote Photoplethysmography (rPPG\index{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\index{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.