Multi patches 3D facial representation for person authentication using AdaBoost

In this paper, we investigate the(problem of 3D face authentication) use of AdaBoost-based learning algorithm which select the relèvent curves in the face. We use the shape of 3D curve for face authentication. The basic idea is the analysis of the shape of local facial set of cuves. The set of curves are extracted using level curves based representation centered on facial feature point landmarks. We apply our framework to study and compare this set through the computation of the geodesic distance between corresponding curves on different 3D facial models. AdaBoost considers each curve as a weak classifier and selects iteratively relevant curves and show that there is significant improvement in results. The effectiveness of this technique is evaluated on a subset taken from BU-3DFE(3D Facial Expression Database — Binghamton University) database. The proposed approach increase authentication performances using AdaBoost classifier compared to a simple fusion of scores from all curves.