Sparse representation for 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the difficulties inherent with 2D face recognition, including the sensitivity to illumination conditions and head pose variations. In spite of their rapid development, many 3D face recognition algorithms in the literature still suffer from the intrinsic complexity in representing and processing 3D facial data. In this paper, we propose a novel sparse representation algorithm for 3D face recognition. The innovation of our approach lies in the strategy of constructing 3D over complete dictionary for 3D face such that 3D sparse representation can be directly used for 3D face recognition. We compare the proposed algorithm to six state-of-the-art algorithms in the FRGC2.0 database. Our results show that the proposed algorithm can substantially improve the efficiency of 3D face recognition.