Distinctiveness of faces: a computational approach

This paper develops and demonstrates an original approach to face-image analysis based on identifying distinctive areas of each
individual’s face by its comparison to others in the population. The method differs from most others—that we refer as unary—
where salient regions are defined by analyzing only images of the same individual. We extract a set of multiscale patches from
each face image before projecting them into a common feature space. The degree of “distinctiveness” of any patch depends on its
distance in feature space from patches mapped from other individuals. First a pairwise analysis is developed and then a simple
generalization to the multiple-face case is proposed. A perceptual experiment, involving 45 observers, indicates the method to be
fairly compatible with how humans mark faces as distinct. A quantitative example of face authentication is also performed in
order to show the essential role played by the distinctive information. A comparative analysis shows that performance of our n-ary
approach is as good as several contemporary unary, or binary, methods, while tapping a complementary source of information.
Furthermore, we show it can also provide a useful degree of illumination invariance.