  In this paper, we introduce FairFaceGAN, a fairness-aware facial
Image-to-Image translation model, mitigating the problem of unwanted
translation in protected attributes (e.g., gender, age, race) during facial
attributes editing. Unlike existing models, FairFaceGAN learns fair
representations with two separate latents - one related to the target
attributes to translate, and the other unrelated to them. This strategy enables
FairFaceGAN to separate the information about protected attributes and that of
target attributes. It also prevents unwanted translation in protected
attributes while target attributes editing. To evaluate the degree of fairness,
we perform two types of experiments on CelebA dataset. First, we compare the
fairness-aware classification performances when augmenting data by existing
image translation methods and FairFaceGAN respectively. Moreover, we propose a
new fairness metric, namely Frechet Protected Attribute Distance (FPAD), which
measures how well protected attributes are preserved. Experimental results
demonstrate that FairFaceGAN shows consistent improvements in terms of fairness
over the existing image translation models. Further, we also evaluate image
translation performances, where FairFaceGAN shows competitive results, compared
to those of existing methods.
