Liveness Detection for Face Recognition (2008)

Photo attack is the cheapest and easiest spoofing approach, since one's facial image is usually very easily available for the public, for example, downloaded from the web, captured unknowingly by a camera. The imposter can rotate, shift and bend the photo before the camera like a live person to fool the authentication system. It is still a challenging task to detect whether an input face image is from a live person or from a photograph. Video spoofing is another big threat to face recognition systems, because it is very similar to live face and can be shot in front of legal user's face by a needle camera. It has many physiological clues that photo does not have, such as head movement, facial expression, blinking et al. 3D model has 3D information of face, however, it is rigid and lack of physiological information. It is also not very easy to be realistic with live person who the 3D model imitates. So photo and video are most common spoofing ways to attack face recognition system. In general, human is able to distinguish a live face and a photograph without any effort, since human can very easily recognize many physiological clues of liveness, for example, facial expression variation, mouth movement, head rotation, eye change. However, the tasks of computing these clues are often complicated for computer, even impossible for some clues under the unconstrained environment. From the static view, an essential difference between a live face and a photograph is that a live face is a fully three dimensional object while a photograph could be considered as a two dimensional planar structure. With this natural trait, Choudhary et al employed the structure from motion yielding the depth information of the face to detect live person or still photo (Choudhary et al., 1999). The disadvantages of depth information are that, firstly it is hard to estimate depth information when head is still. Secondly, the estimate is very sensitive to noise and lighting condition, becoming unreliable. Compared with photographs, another prominent characteristic of live faces is the occurrence of the non-rigid deformation and appearance change, such as mouth motion, expression variation. The accurate and reliable detection of these changes usually needs either the input data of high-quality or user collaboration. Kollreider et al applies the optical flow to the input video to obtain the information of face motion for liveness judgement Some researchers use the multi-modal approaches of face-voice against spoofing This kind of method needs voice recorder and user collaboration. An interactive approach is tried by Frischholz et al, requiring user to act an obvious response of head movement Besides, Li et al presented Fourier spectra to classify live faces or faked images, based on the assumption that the high frequency components of the photo is less than those of live face images With thermal infrared imaging camera, face thermogram also could be applied in to liveness detection Table summaries these anti-spoofing clues, in terms of data quality, hardware and user collaboration, for comparison. www.intechopen.com Liveness Detection for Face Recognition 111 Clues Data Quality Additional Hardware User Collaboration Facial expression High No Middle Depth information High No Low Mouth movement Middle No Middle Head movement High No Middle Eye blinking Low No Low