Expression Recognition Analysis in the Wild

TL;DR

This paper presents a baseline facial expression recognition method using a fine-tuned SeNet model on the AffWild2 dataset, aiming to establish a foundation for future improvements and participating in the ABAW competition.

Contribution

It introduces a baseline approach for in-the-wild facial expression recognition using transfer learning with SeNet, tailored for the ABAW challenge.

Findings

Achieved validation results on the Expression Challenge subset.

Provides a benchmark for future methods in wild FER.

Supports evaluation on the ABAW test set.

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.