In recent years, the automatic face recognition and verification based on 2D optical (intensity or color) image has been actively researched, but it still faces many challenges, such as illumination, expression, and pose variaton. in fact, the human face generates not only 2D texture information but also 3D shape information. in this paper, we presented a novel approach for automatic 3D face verification from range data. the method consists of range data registration and comparison. there are three steps in registration procedure : the coarce step conducting the normalization by exploiting a priori knowledge of the human face and facial features to make faces have the similiar attitude; the next step considering the existence of holes in the range data will undermining the recognition result, we presented a novel approach for holes filling to improve the range data quality; and the fine step aligning the input data with the model in database by the delaunay-interative close point (D-ICP) algorithm. during the face comparison, a modified hausdorff distance (MHD) is employed as the similarity metrics. the experiments are carried out on a database with 30 individuals, and the best EER of 1.667% is achieved.