This paper presents an effective method for 3D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multi-scale extended Local Binary Patterns (eLBP) and enables an efficient and accurate representation of local shape changes; it thus enhances the distinctiveness of smooth and similar facial range images generated by preprocessing steps. The following matching strategy is SIFT-based and is carried out in a hybrid way that combines local and holistic analysis, thereby robustly associating the keypoints between two facial representations of the same subject. As a result, the proposed approach proves to be robust to facial expression variations, partial occlusions and moderate pose changes. This last property makes our system registration-free for nearly frontal face models. The proposed method was experimented on three public datasets, i.e. FRGC v2.0, Gavab and Bosphorus. It displays a rank-one recognition rate of 97.6% and a verification rate of 98.4% at a 0.001 FAR on the FRGC v2.0 database without any face alignment. Additional experiments on the Bosphorus dataset further highlight the advantages of the proposed method with regard to expression changes and external partial occlusions. The last experiment carried out on the Gavab database demonstrates that the entire system can also deal with faces under large pose variations, including even partially occluded ones, when only aided by a coarse alignment process