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With the wide deployment of face recognition systems in applications from border control to mobile device unlocking, the combat of face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays and 3D masks. We address the problem of facial spoofing detection against replay attacks based on the analysis of aliasing in spoof face videos. The application domain of interest is mobile phone unlock. We analyze the moiré pattern aliasing that commonly appears during the recapture of video or photo replays on a screen in different channels (R, G, B and grayscale) and regions (the whole frame, detected face, and facial component between the nose and chin). Multi-scale LBP and DSIFT features are used to represent the characteristics of moiré patterns that differentiate a replayed spoof face from a live face (face present). Experimental results on Idiap replay-attack and CASIA databases as well as a database collected in our laboratory (RAFS), which is based on the MSU-FSD database, shows that the proposed approach is very effective in face spoof detection for both cross-database, and intra-database testing scenarios.
Patel et al. (Fri,) studied this question.