Human Emotion Recognition With Relational Region-Level Analysis
Abstract
Recognizing the emotional state of a person within the image in real-world scenarios is a key problem in affective computing and has various promising applications. Local regions in the image, including different objects in the background scene and parts within the foreground body, usually have different contributions to emotion perception of the target person. This, however, has not been well exploited in most existing methods. In this article, we propose to make relational region-level analysis to account for the different contributions of different regions to emotion recognition. For the background scene, we propose a Body-Object Attention (BOA) module to estimate the contributions of background objects to emotion recognition given the target foreground body. Within the foreground body, we propose a Body Part Attention (BPA) module to recalibrate the channel-wise body feature responses to attend on body parts that are more important. Moreover, we propose to model the emotion label dependency in real-world images, considering both the semantic meanings of these labels and their co-occurrence patterns. We evaluate the proposed method on the EMOTIC and CAER-S datasets, and experimental results show the superiority of our method compared with the state-of-the-art algorithms.
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SAFER outperforms the closest result reported by Li et al. [39] on the CAER-S dataset by 7.8%.
“We see SAFER outperforms closest result reported by Li. et al. [39] by 7.8%.”
From SAFER: Situation Aware Facial Emotion Recognition (2023) · cites this paper as [39]
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SalMIM: Saliency-Guided Masked Image Modeling Network for Visual Emotion Analysis
Peng, W., Zhong, S.-H., Fares, A. H., & Liu, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3641966
Video emotion analysis: Segmentation, classification, and explainability techniques in review
Hallur, S., & Gavade, A. B. (2026) · Franklin Open · doi:10.1016/j.fraope.2026.100604
Context Effect Guided Multi-Source Awareness Framework for Dynamic Facial Expression Recognition
Bao, J., Qian, J., Yan, M., & Yang, J. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3688789
Identity-preserving, emotion-faithful face synthesis via multi-dimensional conditional fusion
Guo, Q., Li, K., & Wang, Z. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115593
Popescu, C.-B., & Florea, C. (2026) · International Conference on Communications (COMM) · doi:10.1109/comm70054.2026.11591288
CEENet: Emotion Recognition with Contrastive Learning and Visual Embeddings
夏, 旭. (2026) · Computer Science and Application · doi:10.12677/csa.2026.167249
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Multi-modal CNN Based Emotion Recognition with Prior Pose Estimation Using YOLOv5
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Who, Where, How Matters: Evaluating Emotion Recognition under Context Shifts
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- Authors
- Li, W., Dong, X., & Wang, Y.
- Affiliations
- Beihang University (CN)
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2023-01-01
- Volume
- 14
- Issue
- 1
- Pages
- 650-663
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W3135844897
- Cited by
- 43
- References
- 62
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 11 · 2023: 3 · 2022: 3 · 2021: 2
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61802026, 61806016)
- Fundamental Research Funds for the Central Universities (2019RC39)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- facial emotion recognition, attention modules, affective computing
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