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Although 2D-based face recognition methods have made great progress in the past decades, there are also some unsolved problems such as PIE. Recently, more and more researchers have focused on 3D-based face recognition approaches. Among these techniques, facial feature point localization plays an important role in representing and matching 3D faces. In this paper, we present a novel feature point localization method on 3D faces combining global shape model and local surface model. Bezier surface is introduced to represent local structure of different feature points and global shape model is utilized to constrain the local search result. Experimental results based on comparison of our method and curvature analysis show the feasibility and efficiency of the new idea.