3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for 2D images were shown sensitive to lighting condition changes. In this paper, we propose a learning-based approach for reliable locating of face landmarks in 3D. Our approach relies on a statistical model, called 3D Statistical Facial Feature Model(SFFM) in the paper, which learns both global variations in 3D face morphology and local ones around the 3D face landmarks in terms of local texture and geometry. Experimented on FRGC v1.0 dataset, our approach shows its effectiveness and achieves 99.09\% of locating accuracy in 10mm precision. The mean error and standard deviation of each landmark are respectively less than 5mm and 4.