In this paper, we introduce a novel shape-based interest point (SIP) descriptor to encode local surface shapes for three-dimensional (3D) ear recognition; the descriptor provides an advantage over previous descriptors by capturing greater details of the macro-shape patterns surrounding an interest point. Using the SIP descriptor, a function is developed to measure the shape dissimilarity between any two interest points. Finally, in the recognition stage, a probe and a gallery pair are compared by applying the matching algorithm on the interest points, with the similarity score set as the number of matched interest points. The proposed method has been tested on the University of Notre Dame(UND) collection J2 dataset, containing range images of 415 subjects. The experimental results demonstrate that our method achieves a 97.4% rank-one recognition rate and a 2.0% Equal Error Rate (EER), which outperforms the state-of-the-art methods.