Expression Recognition Analysis in the Wild

Expression Recognition Analysis in the Wild

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-trai...

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Hauptverfasser: Cafarelli, Donato, Massoli, Fabio Valerio, Falchi, Fabrizio, Gennaro, Claudio, Amato, Giuseppe
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Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
Computer Science - Learning
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creator Cafarelli, Donato
Massoli, Fabio Valerio
Falchi, Fabrizio
Gennaro, Claudio
Amato, Giuseppe
description 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.
doi_str_mv 10.48550/arxiv.2101.09231
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Computer Science - Human-Computer Interaction
Computer Science - Learning
title Expression Recognition Analysis in the Wild
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