  There are demographic biases in the SOTA CNN used for FR. Our BFW dataset
serves as a proxy to measure bias across ethnicity and gender subgroups,
allowing us to characterize FR performances per subgroup. We show performances
are non-optimal when a single score threshold is used to determine whether
sample pairs are genuine or imposter. Furthermore, actual performance ratings
vary greatly from the reported across subgroups. Thus, claims of specific error
rates only hold true for populations matching that of the validation data. We
mitigate the imbalanced performances using a novel domain adaptation learning
scheme on the facial encodings extracted using SOTA deep nets. Not only does
this technique balance performance, but it also boosts the overall performance.
A benefit of the proposed is to preserve identity information in facial
features while removing demographic knowledge in the lower dimensional
features. The removal of demographic knowledge prevents future potential biases
from being injected into decision-making. Additionally, privacy concerns are
satisfied by this removal. We explore why this works qualitatively with hard
samples. We also show quantitatively that subgroup classifiers can no longer
learn from the encodings mapped by the proposed.
