  As face recognition algorithms become more accurate and get deployed more
widely, it becomes increasingly important to ensure that the algorithms work
equally well for everyone. We study the geographic performance
differentials-differences in false acceptance and false rejection rates across
different countries-when comparing selfies against photos from ID documents. We
show how to mitigate geographic performance differentials using sampling
strategies despite large imbalances in the dataset. Using vanilla domain
adaptation strategies to fine-tune a face recognition CNN on domain-specific
doc-selfie data improves the performance of the model on such data, but, in the
presence of imbalanced training data, also significantly increases the
demographic bias. We then show how to mitigate this effect by employing
sampling strategies to balance the training procedure.
