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Pulse-based Features for Face Presentation Attack Detection

In this contribution, we propose to tackle the face presentation attack detection (PAD) problem by using features derived from a pulse signal obtained through remote photoplesthymography (rPPG). Recent studies show that the pulse signal provides information on the liveness of a subject; hence it can be used to identify whether a recorded video sequence originates from a genuine user or is an attack. 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. Experiments are performed on different, publicly available databases and following associated protocols. 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.