  In recent years, media reports have called out bias and racism in face
recognition technology. We review experimental results exploring several
speculated causes for asymmetric cross-demographic performance. We consider
accuracy differences as represented by variations in non-mated (impostor) and /
or mated (genuine) distributions for 1-to-1 face matching. Possible causes
explored include differences in skin tone, face size and shape, imbalance in
number of identities and images in the training data, and amount of face
visible in the test data ("face pixels"). We find that demographic differences
in face pixel information of the test images appear to most directly impact the
resultant differences in face recognition accuracy.
