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Automatic face recognition is becoming increasingly important due to the security applications derived from it. Although the facial recognition problem has focused on 2D images, recently, due to the proliferation of 3D scanning hardware, 3D face recognition has become a feasible application. This 3D approach does not need any color information. In this way, it has the following main advantages in comparison to more traditional 2D approaches: (1) being robust under lighting variations and (2) providing more relevant information. In this paper we present a new 3D facial model based on the curvature properties of the surface. Our system is able to detect the subset of the characteristics of the face with higher discrimination power from a large set. The robustness of the model is tested by comparing recognition rates using both controlled and noncontrolled environments regarding facial expressions and facial rotations. The difference between the recognition rates of the two environments of only 5% proves that the model has a high degree of robustness against pose and facial expressions. We consider that this robustness is enough to implement facial recognition applications, which can achieve up to 91% correct recognition rate. A publish 3D face database containing face rotations and expressions has been created to achieve the recognition experiments.

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.