Countermeasures to face photo spoofing attacks by exploiting structure and texture information from rotated face sequences
This work focuses on the most common and cheapest face spoofing methods, i.e., photo attacks (including the printed photo on a paper or a photo demonstrated on an electronic screen). Many previous works [3-6] propose to classify genuine and fake samples based on frontal face images and achieve good performance on several face spoofing databases. However, in real applications, the imposter will try his best to fool the system and the texture difference between the genuine and fake samples is usually very small. In order to achieve robust face anti-spoofing performance, other cues like 3D face structure and motion pattern can be incorporated. In this work, we propose to detect spoofing photo attacks based on a sequence of rotated face images. Both the structure and texture information from the rotated face sequence are exploited. In practice, the users are only asked to take simple movement (i.e., rotate their faces). As pointed in [7], this head rotation requirement is much simpler than traditional challenge-response-based face anti-spoofing method, in which a combination of multiple movements is usually necessary. The proposed anti-spoofing method is applicable to face recognition applications such as face access control and remote authentication on mobile devices. The simple head rotation requirement is acceptable in these applications.
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