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Face recognition is a focused issue in pattern recognition over the past decades. In this paper, we have proposed a new scheme for face recognition using 3D information. In this scheme, the scattered 3D point cloud is first represented with a regular mesh using hierarchical mesh fitting. Then the local shape variation information is extracted to characterize the individual together with the global geometric features. Experimental results on 3D_RMA, a likely largest 3D face database available currently, demonstrate that the local shape variation information is very important to improve the recognition accuracy and that the proposed algorithm has promising performance with a low computational cost.