In recent years, 3D face recognition has been considered as a major solution to deal with these unsolved issues of reliable 2D face recognition, i.e. illumination and pose variations. This paper focuses on two critical aspects of 3D face recognition: facial feature description and classifier design. To address the former one, a novel local descriptor, namely Local Shape Patterns (LSP), is proposed. Since LSP operator extracts both differential structure and orientation information, it can describe local shape attributes comprehensively. For the latter one, Sparse Representation Classifier (SRC) is applied to classify these 3D shape-based facial features. Recently, SRC has been attracting more and more attention of researchers for its powerful ability on 2D image-based face recognition. This paper continues to investigate its competency in shape-based face recognition. The proposed approach is evaluated on the IV2 3D face database containing rich facial expression variations, and promising experimental results are achieved which prove its effectiveness for 3D face recognition and insensitiveness to expression changes