  Human face features can be used to determine individual identity as well as
demographic information like gender and race. However, the extent to which
black-box commercial face recognition algorithms (CFRAs) use gender and race
features to determine identity is poorly understood despite increasing
deployments by government and industry. In this study, we quantified the degree
to which gender and race features influenced face recognition similarity scores
between different people, i.e. non-mated scores. We ran this study using five
different CFRAs and a sample of 333 diverse test subjects. As a control, we
compared the behavior of these non-mated distributions to a commercial iris
recognition algorithm (CIRA). Confirming prior work, all CFRAs produced higher
similarity scores for people of the same gender and race, an effect known as
"broad homogeneity". No such effect was observed for the CIRA. Next, we applied
principal components analysis (PCA) to similarity score matrices. We show that
some principal components (PCs) of CFRAs cluster people by gender and race, but
the majority do not. Demographic clustering in the PCs accounted for only 10 %
of the total CFRA score variance. No clustering was observed for the CIRA. This
demonstrates that, although CFRAs use some gender and race features to
establish identity, most features utilized by current CFRAs are unrelated to
gender and race, similar to the iris texture patterns utilized by the CIRA.
Finally, reconstruction of similarity score matrices using only PCs that showed
no demographic clustering reduced broad homogeneity effects, but also decreased
the separation between mated and non-mated scores. This suggests it's possible
for CFRAs to operate on features unrelated to gender and race, albeit with
somewhat lower recognition accuracy, but that this is not the current
commercial practice.
