FindingEmo: An Image Dataset for Emotion Recognition in the Wild

Laurent Mertens, Elahe Yargholi, H. O. D. Beeck, Jan Van den Stock, J. Vennekens
2/3/2026

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

MMIS: Multimodal Dataset for Interior Scene Visual Generation and Recognition

83% match1 repo112024

UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment

82% match2024

MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context Understanding

82% match1 repo22024

UnCommon Objects in 3D

81% match1 repo1.4k2026

Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

80% match1 repo1362026

Can Vision-Language Models Replace Human Annotators: A Case Study with CelebA Dataset

80% match1 repo2024

Cite this paper

@article{mertens2026findingemo,
  title  = {FindingEmo: An Image Dataset for Emotion Recognition in the Wild},
  author = {Laurent Mertens and Elahe Yargholi and H. O. D. Beeck and Jan Van den Stock and J. Vennekens},
  year   = {2026},
  eprint = {2402.01355},
  archivePrefix = {arXiv},
  doi    = {10.48550/arXiv.2402.01355},
  url    = {https://arxiv.org/abs/2402.01355},
  journal = {NEURIPS 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2402.01355.svg)](https://codeofpaper.com/paper/2402.01355)

Discussion