Holistic processing develops because it is good

In this paper, we investigate the question, “what are the best features for face identification? ” Ullman et al. used mutual information as measurement of how good a feature is for a class [Ullman et al., 2002]. Their ex-periments suggested that features of intermediate com-plexity are best in tasks of face vs. non-face and cars vs. non-cars. We are interested in the tasks of face identification and expression classification. We applied Ullman’s technique of finding features with high mu-tual information with category labels to the tasks. We found that features of large sizes convey the most in-formation about face identity. Local features such as eyes and mouth are informative for identity in the con-text of large face areas. Yet they are not very infor-mative by themselves, especially for an image set with high variability of facial expressions. On the other hand, small sized features around the eyes and mouth contain relatively high information for expression classification. This suggests that the appropriate feature sizes are task dependent. We suggest that holistic processing of faces has developed because these features are optimal for face identification