Secure Smartphone Unlock : Robust Face Spoof Detection on Mobile

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 facial spoof detection against print (photo) and replay (photo or video) attacks based on the analysis of image aliasing (e.g., surface reflection, moiré pattern, color distortion, and shape deformation) in spoof face images (or video frames). The application domain of interest is mobile phone unlock, given that growing number of phones have face unlock and mobile payment capabilities. We build a mobile spoof face database (MSU MSF) containing more than 1, 000 subjects, which is, to our knowledge, the largest spoof face database in terms of the number of subjects. Both print and replay attacks are captured using the front and rear cameras of a Nexus 5 phone. We analyze the aliasing of print and replay attacks using (i) different intensity channels (R, G, B and grayscale), (ii) different image regions (entire image, detected face, and facial component between the nose and chin), and (iii) different feature descriptors. We develop an efficient face spoof detection system on an Android smartphone. Experimental results on three public-domain face spoof databases (Idiap Print-Attack and Replay-Attack, and CASIA), and the MSU MSF 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.