Zero-shot demographically unbiased image generation from an existing
biased StyleGAN
Abstract
Face recognition systems have made significant strides thanks to
data-heavy deep learning models, but these models rely on large
privacy-sensitive datasets. Recent work in facial analysis and
recognition have thus started making use of synthetic datasets generated
from GANs and diffusion based generative models. These models, however,
lack fairness in terms of demographic representation and can introduce
the same biases in the trained downstream tasks. This can have serious
societal and security implications. To address this issue, we propose a
methodology that generates unbiased data from a biased generative model
using an evolutionary algorithm. We show results for StyleGAN2 model
trained on the Flicker Faces High Quality dataset to generate data for
singular and combinations of demographic attributes such as
\textit{Black and Woman}. We generate a large racially
balanced dataset of 13.5 million images, and show that it boosts the
performance of facial recognition and analysis systems whilst reducing
their biases.Â