3D facial data is potential to overcome the problems of illumination and pose variation in face recognition. In this paper, we investigate face recognition from range data by facial profiles and surface. An efficient symmetry plane detection method for facial range data is presented to help extract facial profile. A global profile matching method is exploited to align and compare two profiles, without detection of fiducial points that is often unreliable. The central profile and two kinds of horizontal profiles-nose-crossing profile and forehead-crossing profile, are employed in recognition. For each individual, a statistical model is built to represent the distinct discriminative capability of the different regions on the facial surface, then is incorporated into a weighted distance function to measure similarity of surfaces. The comparable experimental results are achieved on a facial range data database with 120 individuals.