  Whilst face recognition applications are becoming increasingly prevalent
within our daily lives, leading approaches in the field still suffer from
performance bias to the detriment of some racial profiles within society. In
this study, we propose a novel adversarial derived data augmentation
methodology that aims to enable dataset balance at a per-subject level via the
use of image-to-image transformation for the transfer of sensitive racial
characteristic facial features. Our aim is to automatically construct a
synthesised dataset by transforming facial images across varying racial
domains, while still preserving identity-related features, such that racially
dependant features subsequently become irrelevant within the determination of
subject identity. We construct our experiments on three significant face
recognition variants: Softmax, CosFace and ArcFace loss over a common
convolutional neural network backbone. In a side-by-side comparison, we show
the positive impact our proposed technique can have on the recognition
performance for (racial) minority groups within an originally imbalanced
training dataset by reducing the pre-race variance in performance.
