  Similarity scores in face recognition represent the proximity between pairs
of images as computed by a matching algorithm. Given a large set of images and
the proximities between all pairs, a similarity score space is defined. Cluster
analysis was applied to the similarity score space to develop various
taxonomies. Given the number of subjects in the dataset, we used hierarchical
methods to aggregate images of the same subject. We also explored the hierarchy
above and below the subject level, including clusters that reflect gender and
ethnicity. Evidence supports the existence of clustering by race, gender,
subject, and illumination condition.
