Overcoming deep learning subclass imbalances: Comparing the transfer of identity across a racial transformation.

As facial authentication systems become an increasingly advantageous technology, the subtle inaccuracy under certain subgroups grows in importance. As researchers perform data augmentation to increase subgroup accuracies, it is critical that the data augmentation approaches are understood. We specifically research the impact that the data augmentation method of racial transformation has upon the identity of the individual according to a facial authentication network. This demonstrates whether the racial transformation maintains critical aspects to an individual identity or whether the data augmentation method creates the equivalence of an entirely new individual for networks to train upon. We demonstrate our method for racial transformation based on other top research articles methods, display the embedding distance distribution of augmented faces compared with the embedding distance of non-augmented faces and explain to what extent racial transformation maintains critical aspects to an individual’s identity.