In this paper, we discuss new experiments on face recognition and authentication based on dimensional surface matching. While most of existing methods use facial intensity images, a newest ones focus on introducing depth information to surmount some of classical face recognition problems such as pose, illumination, and facial expression variations. The presented matching algorithm is based on ICP (Iterative Closest Point) that provides perfectly the posture of presented probe. In addition, the similarity metric is given by spatial deviation between the overlapped parts in matched surfaces. The general paradigm consists in building a full 3D face gallery using a laser-based scanner (the off-line phase). At the on-line phase, identification or verification, only one captured 2.5D face model is performed with the whole set of 3D faces from the gallery or compared to the 3D face model of the genuine, respectively. This probe model can be acquired from arbitrary viewpoint, with arbitrary facial expressions, and under arbitrary lighting conditions.

A new multi-view registered 3D face database, including these variations, is developed within BioSecure Workshop 2005 in order to perform significant experiments.