Discriminative face recognition

A novel probabilistic deformable model of face mapping was recently introduced and successfully applied to automatic person identification. In this paper, we consider the use of discrimination to improve the performance of this system. It is possible to introduce discriminative information at two different levels: 1) in the face representations and 2) in the deformable model used to match face images. We explore both types of discrimination and compare them in terms of performance and computational complexity. Results are presented on the FERET face database for a face identification task and show that, in this framework and for the discriminative techniques that were considered, the discrimination of the deformable model should be preferred and can result in a 25-40% relative error rate reduction compared to the baseline system.