Face Authentication Based on Multiple Profiles Extracted from Range Data

In this paper we address face authentication based on profiles extracted from range data. Three kinds of profiles are defined, then extracted, and are combined to classify. For obtaining central profile, a novel robust symmetry plane detection method is proposed. A global profile matching approach based on the partial Hausdorff metric is presented to align and compare profiles, without detection of fiducial points that is often unreliable. To utilize more information in range data, we extract nose-crossing profile and forehead-crossing profile, more than only central profile. The experiments are carried out on a low-quality database with 180 pieces of range data of 30 individuals acquired by structured light system. Based on the experimental results, we observe that the presented scheme can cope with limited quality of facial range data.