Understanding Pose Discrimination in Similarity Space

Identity-independent estimation of head pose from prototype im-ages is a perplexing task, requiring pose-invariant face detection. The problem is exacerbated by changes in illumination, identity and facial position. Facial images must be transformed in such a way as to em-phasise dierences in pose, while suppressing dierences in identity. We investigate appropriate transformations for use with a similarity-to-prototypes philosophy. The results show that orientation-selective Gabor lters enhance dierences in pose, and that dierent lter ori-entations are optimal at dierent poses. In contrast, PCA was found to provide an identity-invariant representation in which similarities can be calculated more robustly. We also investigate the angular resolution at which pose changes can be resolved using our methods. An angular resolution of 10 was found to be suciently discriminable at some poses but not at others, while 20 is quite acceptable at most poses. 1