  Facial recognition systems have made significant strides thanks to data-heavy
deep learning models, but these models rely on large privacy-sensitive
datasets. Further, many of these datasets lack diversity in terms of ethnicity
and demographics, which can lead to biased models that can have serious
societal and security implications. To address these issues, we propose a
methodology that leverages the biased generative model StyleGAN2 to create
demographically diverse images of synthetic individuals. The synthetic dataset
is created using a novel evolutionary search algorithm that targets specific
demographic groups. By training face recognition models with the resulting
balanced dataset containing 50,000 identities per race (13.5 million images in
total), we can improve their performance and minimize biases that might have
been present in a model trained on a real dataset.
