HEAD POSE ESTIMATION IN FACE RECOGNITION ACROSS POSE SCENARIOS

Abstract

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 colour etc., is proposed. Using such features we generate a pose similarity feature space (PSFS) that turns the multi-class problem into two-class by using inter-pose and intra-pose 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 CMU Pose, Illumination and Expression (PIE) database.