Understanding Pose Discrimination in Similarity Space

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. Facial images must be transformed in such a way as to emphasise di erences in pose, while suppressing di erences in identity. We investigate appropriate transformations for use with a similarityto-prototypes philosophy. The results show that orientation-selective Gabor lters enhance di erences in pose, and that di erent lter orientations are optimal at di erent 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 su ciently discriminable at some poses but not at others, while 20 is quite acceptable at most poses.