  Several research groups have shown that Generative Adversarial Networks
(GANs) can generate photo-realistic images in recent years. Using the GANs, a
map is created between a latent code and a photo-realistic image. This process
can also be reversed: given a photo as input, it is possible to obtain the
corresponding latent code. In this paper, we will show how the inversion
process can be easily exploited to interpret the latent space and control the
output of StyleGAN2, a GAN architecture capable of generating photo-realistic
faces. From a biological perspective, facial features such as nose size depend
on important genetic factors, and we explore the latent spaces that correspond
to such biological features, including masculinity and eye colour. We show the
results obtained by applying the proposed method to a set of photos extracted
from the CelebA-HQ database. We quantify some of these measures by utilizing
two landmarking protocols, and evaluate their robustness through statistical
analysis. Finally we correlate these measures with the input parameters used to
perturb the latent spaces along those interpretable directions. Our results
contribute towards building the groundwork of using such GAN architecture in
forensics to generate photo-realistic faces that satisfy certain biological
attributes.
