Expression Recognition Analysis in the Wild
This work provides a baseline for future methods in facial expression recognition, addressing human-computer interaction needs, but it is incremental as it applies existing techniques to a specific dataset.
The paper tackles facial expression recognition in the wild by fine-tuning a SeNet architecture on the AffWild2 dataset, achieving results on the validation set for the ABAW competition's Expression Challenge, with plans to update based on test set leaderboard data.
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.