Face distributions in similarity space under varying head pose

摘要

Real-time identity-independent estimation of head pose from prototype images is a perplexing task requiring pose-invariant face detection. The problem is exacerbated by changes in illumination, identity and facial position. We approach the problem using a view-based statistical Learning technique based on similarity of images to prototypes. For this method to be effective, facial images must be transformed in such a Way as to emphasise differences in pose while suppressing differences in identity. We investigate appropriate transformations for use with a Similarity-to-prototypes philosophy.