An efficient 3D partial face recognition approach with single sample

3D partial face recognition under missing parts, occlusions and data corruptions is a major challenge for the practical application of the techniques of 3D face recognition. Moreover, one individual can only provide one sample for training in most practical scenarios, and thus the face recognition with single sample problem is another highly challenging task. We propose an efficient framework for 3D partial face recognition with single sample addressing both of the two problems. First, we represent a facial scan with a set of keypoint based local geometrical descriptors, which gains sufficient robustness to partial facial data along with expression/pose variations. Then, a two-step modified collaborative representation classification scheme is proposed to address the single sample recognition problem. A class-based probability estimation is given during the first classification step, and the obtained result is then incorporated into the modified collaborative representation classification as a locality constraint to improve its classification performance. Extensive experiments on the Bosphorus and FRGC v2.0 datasets demonstrate the efficiency of the proposed approach when addressing the problem of 3D partial face recognition with single sample.