With the wide deployment of face recognition systems in applications from de-duplication to mobile device unlocking, security against face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays and 3D masks of a face. We address the problem of face spoof detection against print (photo) and replay (photo or video) attacks based on the analysis of image distortion (e.g., surface reflection, moiré pattern, color distortion, and shape deformation) in spoof face images (or video frames). The application domain of interest is smartphone unlock, given that growing number of smartphones have face unlock and mobile payment capabilities. We build an unconstrained smartphone spoof attack database (MSU USSA) containing more than 1, 000 subjects. Both print and replay attacks are captured using the front and rear cameras of a Nexus 5 smartphone. We analyze the image distortion of print and replay attacks using different (i) intensity channels (R, G, B and grayscale), (ii) image regions (entire image, detected face, and facial component between the nose and chin), and (iii) feature descriptors. We develop an efficient face spoof detection system on an Android smartphone. Experimental results on the public-domain Idiap Replay-Attack, CASIA FASD, and MSU-MFSD databases, and the MSU USSA database show that the proposed approach is effective in face spoof detection for both cross-database and intra-database testing scenarios. User studies of our Android face spoof detection system involving 20 participants show that the proposed approach works very well in real application scenarios.Index Terms-Face antispoofing, face unlock, spoof detection on smartphone, unconstraint smartphone spoof attack database, image distortion analysis

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