  In this project, we created a database with two types of annotations used in
the emotion recognition domain : Action Units and Valence Arousal to try to
achieve better results than with only one model. The originality of the
approach is also based on the type of architecture used to perform the
prediction of the emotions : a categorical Generative Adversarial Network. This
kind of dual network can generate images based on the pictures from the new
dataset thanks to its generative network and decide if an image is fake or real
thanks to its discriminative network as well as help to predict the annotations
for Action Units and Valence Arousal due to its categorical nature. GANs were
trained on the Action Units model only, then the Valence Arousal model only and
then on both the Action Units model and Valence Arousal model in order to test
different parameters and understand their influence. The generative and
discriminative aspects of the GANs have performed interesting results.
