Learning Probabilistic Models for Recognizing Faces under Pose Variations

Recognizing a face from a novel view point poses major challenges for automatic face recognition. Recent methods address this problem by trying to model the subject specific appearance change across pose. For this, however, almost all of the existing methods require a perfect alignment between a gallery and a probe image. In this paper we present a pose invariant face recognition method centered on modeling joint appearance of gallery and probe images across pose in a probabilistic framework. We propose novel extensions in this direction by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features we put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment between gallery and probe images which makes it particularly attractive as compared to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions.