  Facial Expression Recognition(FER) is one of the most important topic in
Human-Computer interactions(HCI). In this work we report details and
experimental results about a facial expression recognition method based on
state-of-the-art methods. We fine-tuned a SeNet deep learning architecture
pre-trained on the well-known VGGFace2 dataset, on the AffWild2 facial
expression recognition dataset. The main goal of this work is to define a
baseline for a novel method we are going to propose in the near future. This
paper is also required by the Affective Behavior Analysis in-the-wild (ABAW)
competition in order to evaluate on the test set this approach. The results
reported here are on the validation set and are related on the Expression
Challenge part (seven basic emotion recognition) of the competition. We will
update them as soon as the actual results on the test set will be published on
the leaderboard.
