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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Video emotion recognition (VER), situated at the convergence of affective computing and computer vision, aims to predict the primary emotion evoked in most viewers through video content, with extensive applications in video recommendation, human–computer interaction, and intelligent education. This paper commences with an analysis of the psychological models that constitute the foundation of VER theory. The paper further elaborates on datasets and evaluation metrics commonly utilized in VER. Then, the paper reviews VER algorithms according to their categories, and compares and analyzes the experimental results of classic methods on four datasets. Based on a comprehensive analysis and investigations, the paper identifies the prevailing challenges currently faced in the VER field, including gaps between emotional representations and labels, large-scale and high-quality VER datasets, and the efficient integration of multiple modalities. Furthermore, this study proposes potential research directions to address these challenges, e.g., advanced neural network architectures, efficient multimodal fusion strategies, high-quality emotional representation, and robust active learning strategies.

Details

Title
Advances in Video Emotion Recognition: Challenges and Trends
Author
Yun, Yi 1   VIAFID ORCID Logo  ; Zhou Yunkang 2   VIAFID ORCID Logo  ; Wang Tinghua 1   VIAFID ORCID Logo  ; Zhou, Jin 2   VIAFID ORCID Logo 

 School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China, Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, China 
 School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China 
First page
3615
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3223941908
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.