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Hauptverfasser: Mertens, Laurent, Yargholi, Elahe', de Beeck, Hans Op, Stock, Jan Van den, Vennekens, Joost
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2402.01355
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author Mertens, Laurent
Yargholi, Elahe'
de Beeck, Hans Op
Stock, Jan Van den
Vennekens, Joost
author_facet Mertens, Laurent
Yargholi, Elahe'
de Beeck, Hans Op
Stock, Jan Van den
Vennekens, Joost
contents We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.
format Preprint
id arxiv_https___arxiv_org_abs_2402_01355
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle FindingEmo: An Image Dataset for Emotion Recognition in the Wild
Mertens, Laurent
Yargholi, Elahe'
de Beeck, Hans Op
Stock, Jan Van den
Vennekens, Joost
Computer Vision and Pattern Recognition
Artificial Intelligence
We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.
title FindingEmo: An Image Dataset for Emotion Recognition in the Wild
topic Computer Vision and Pattern Recognition
Artificial Intelligence
url https://arxiv.org/abs/2402.01355