Abstract

Head pose estimation is an important task for many face analysis applications, such as face recognition systems and human com- puter interactions. In this paper we aim to address the pose estimation problem under some challenging conditions, e.g., from a single image, large pose variation, and un-even illumination conditions. The approach we developed combines non-linear dimension reduction techniques with a learned distance metric transformation. The learned distance metric provides better intra-class clustering, therefore preserving a smooth low- dimensional manifold in the presence of large variation in the input im- ages due to illumination changes. Experiments show that our method improves the performance, achieving accuracy within 2-3 degrees for face images with varying poses and within 3-4 degrees error for face images with varying pose and illumination changes.