3d Face Recognition from Range Data

3D facial data has a great potential in overcoming 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 then exploited to align and compare the two profiles without detecting 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. It is then incorporated into a weighted distance function to measure for the similarity of surfaces. The comparable experimental results are achieved on a facial range data database with 120 individuals.