An efficient front-end facial pose estimation system for face recognition

We present a robust front-end pose classification/estimation procedure to be used in face recognition scenarios. A novel discriminative feature description that encodes underlying shape well and is insensitive to illumination and other common variations in facial appearance, such as skin color etc., is proposed. Using such features we generate a pose similarity feature space (PSFS) that turns the multiclass problem into two-class by using interpose and intrapose similarities. A new classification procedure is laid down which models this feature space and copes well with discriminating between nearest poses. For a test image it outputs a measure of confidence or so called posterior probability for all poses without explicitly estimating underlying densities. The pose estimation system is evaluated using the CMU Pose, Illumination, and Expression (PIE) data-base. © 2008 Pleiades Publishing, Ltd.