Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze and eye-blink), and texture-based features. Experimental results show that the proposed method outperforms several other presentation attack detection methods in the literature, which utilize rPPG.
GÜNDOĞAR, MEHMET FATİH and ERDEM, ÇİĞDEM EROĞLU
"Presentation attack detection for face recognition using remotephotoplethysmography and cascaded fusion,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
7, Article 22.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss7/22