Expression Recognition Analysis in the Wild
Abstract
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.
Cite
@article{arxiv.2101.09231,
title = {Expression Recognition Analysis in the Wild},
author = {Donato Cafarelli and Fabio Valerio Massoli and Fabrizio Falchi and Claudio Gennaro and Giuseppe Amato},
journal= {arXiv preprint arXiv:2101.09231},
year = {2021}
}