Towards general motion-based face recognition

Motion-based face recognition is a young research topic, inspired mainly by psychological studies on motion-based perception of human faces. Unlike its close relative, appearance-based face recognition, motion-based face recognition extracts personal characteristics from facial motion (e.g. smile) and uses the information to recognize human identity. However, existing studies in this field are limited to fixed motion, that is - a subject must perform a specific type of facial motion in order to be correctly recognized. In this paper, we try to overcome this limitation by investigating the patterns of local skin deformation exhibited in facial motion. We are pushing the state-of-the-art towards general motion-based face recognition. Our approach is able to extract identity evidence from various types of facial motion, as long as those facial motions are at least, in some part of the face, locally similar to the facial motions used in training. We call our approach Local Deformation Profile (or LDP). This approach is tested through several experiments conducted over a video database of facial expression. The experiment results demonstrate the potential of LDP to be used for biometrics. We also evaluate LDP under extremely heavy face makeup, showing its usefulness to recognize faces even in disguise.