Dynamic approach for face recognition using digital image skin correlation

With the recent emphasis on homeland security, there is an increased interest in accurate and non-invasive techniques for face recognition. Most of the current techniques perform a structural analysis of facial features from still images. Recently, video-based techniques have also been developed but they suffer from low image-quality. In this paper, we propose a new method for face recognition, called Digital Image Skin Correlation (DISC), which is based on dynamic instead of static facial features. DISC tracks the motion of skin pores on the face during a facial expression and obtains a vector field that characterizes the deformation of the face. Since it is almost impossible to imitate another person's facial expressions these deformation fields are bound to be unique to an individual. To test the performance of our method in face recognition scenarios, we have conducted experiments where we presented individuals wearing heavy make-up as disguise to our DISC matching framework. The results show superior face recognition performance when compared to the popular PCA+ LDA method, which is based on still images.