  In the realm of emotion synthesis, the ability to create authentic and
nuanced facial expressions continues to gain importance. The GANmut study
discusses a recently introduced advanced GAN framework that, instead of relying
on predefined labels, learns a dynamic and interpretable emotion space. This
methodology maps each discrete emotion as vectors starting from a neutral
state, their magnitude reflecting the emotion's intensity. The current project
aims to extend the study of this framework by benchmarking across various
datasets, image resolutions, and facial detection methodologies. This will
involve conducting a series of experiments using two emotional datasets:
Aff-Wild2 and AffNet. Aff-Wild2 contains videos captured in uncontrolled
environments, which include diverse camera angles, head positions, and lighting
conditions, providing a real-world challenge. AffNet offers images with
labelled emotions, improving the diversity of emotional expressions available
for training. The first two experiments will focus on training GANmut using the
Aff-Wild2 dataset, processed with either RetinaFace or MTCNN, both of which are
high-performance deep learning face detectors. This setup will help determine
how well GANmut can learn to synthesise emotions under challenging conditions
and assess the comparative effectiveness of these face detection technologies.
The subsequent two experiments will merge the Aff-Wild2 and AffNet datasets,
combining the real world variability of Aff-Wild2 with the diverse emotional
labels of AffNet. The same face detectors, RetinaFace and MTCNN, will be
employed to evaluate whether the enhanced diversity of the combined datasets
improves GANmut's performance and to compare the impact of each face detection
method in this hybrid setup.
