This paper presents a simple yet effective approach for 3D face recognition. A novel 3D facial surface representation, namely Multi-Scale Local Binary Pattern (MS-LBP) Depth Map, is proposed, which is used along with the Shape Index (SI) Map to increase the distinctiveness of smooth range faces. Scale In-variant Feature Transform (SIFT) is introduced to extract local features to enhance the robustness to pose variations. Moreover, a hybrid matching is designed for a further improved accuracy. The matching scheme combines local and holistic analysis. The former is achieved by comparing the SIFT-based features ex-tracted from both 3D facial surface representations; while the latter performs a global constraint using facial component and configuration. Compared with the state-of-the-art, the proposed method does not require time-consuming accurate registration or any additional data in a bootstrap for training special thresholds. The rank-one recognition rate achieved on the complete FRGC v2.0 database is 96.1%. As a result of using local facial features, the approach proves to be competent for dealing with partially occluded face probes as highlighted by supplementary experiments using face masks.