  Synthetically generated images can be used to create media content or to
complement datasets for training image analysis models. Several methods have
recently been proposed for the synthesis of high-fidelity face images; however,
the potential biases introduced by such methods have not been sufficiently
addressed. This paper examines the bias introduced by the widely popular
StyleGAN2 generative model trained on the Flickr Faces HQ dataset and proposes
two sampling strategies to balance the representation of selected attributes in
the generated face images. We focus on two protected attributes, gender and
age, and reveal that biases arise in the distribution of randomly sampled
images against very young and very old age groups, as well as against female
faces. These biases are also assessed for different image quality levels based
on the GIQA score. To mitigate bias, we propose two alternative methods for
sampling on selected lines or spheres of the latent space to increase the
number of generated samples from the under-represented classes. The
experimental results show a decrease in bias against underrepresented groups
and a more uniform distribution of the protected features at different levels
of image quality.
