Human face is intrinsically 3D deformable object with texture. The 3D shape information should not be ignored in face recognition since they provide another type of distinct feature to distinguish different faces. 3D face recognition based on 3D facial information is believed to own the potential to solve the bottleneck in 2D face recognition. Its research is getting more and more attention, and primary research shows promising effects. The research points in 3D face recognition mainly include feature points detection, mesh modeling, noise removing, feature extraction, classifier design and fusion with 2D face. In this thesis, based on the depth image, we focus on three of the problems: feature extraction, classifier design and fusion. Here lists the main work and our contributions in this thesis: 1. We review almost all appeared algorithms on 3D face recognition and 2D+3D multi-modal face recognition. This review can help us realize the state of art in this research area, and provide a base for us to do further research. 2. We propose a new expressive feature for 3D face recognition which bases on the combination of global statistics of geometrical features and local statistics of correlative features. Correlative feature is introduced and analyzed detailedly, and 3DLBP descriptor is proposed to encode correlative features. We believe correlative feature and traditional geometrical feature can be complementary to describe 3D face, and the combination of both features is demonstrated to own great discriminating power. 3. We propose a simple but effective method to discriminating 3D faces.the method is based on the statistics of depth image differences.After analyzing the physical meanings of facial depth images, we believe the difference from depth image substraction can represent the difference between 3D faces. By using histogram proportion, andmaking two improvements to solve the problems caused by registration errors and expression variation, we finally achieve a new recognition method. It is simple, straightforward but works well. In a word, in this thesis, we have made some fruitful attempts and significant progresses on 3D face recognition.
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