Abstract: This paper proposes an approach of face recognition using composite features.This approach first aligned 3D faces based on non-uniform mesh re-sampling which result the uniform number of vertex and topology structure and kept in a vector.Second, we changed the vector to the matrix by division and overlap, and then 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner.This approach not only avoided face information missing, increased the number of composite feature, but also avoided the small sample size problem in theory.Experimental results for 3D face data set from BJUT-3D face database have demonstrated the performance of our algorithm.