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From individual to group-level emotion recognition: EmotiW 5.0
Open accessAbstract
Research in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects of affective computing. This year there are two sub-challenges: a) Audio-video emotion recognition and b) group-level emotion recognition. These challenges are based on the acted facial expressions in the wild and group affect databases, respectively. The particular focus of the challenge is to evaluate method in `in the wild' settings. `In the wild' here is used to describe the various environments represented in the images and videos, which represent real-world (not lab like) scenarios. The baseline, data, protocol of the two challenges and the challenge participation are discussed in detail in this paper.
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Self-citation
Research works printed but not linked: 2
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Mou, W., Güneş, H., & Patras, I. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3321509 · Cited by 20
Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction
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Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition
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DisguiseNet: A Contrastive Approach for Disguised Face Verification in the Wild
Peri, S. V., & Dhall, A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00011 · Cited by 17
Self-citationSemi-supervised learning for facial expression-based emotion recognition in the continuous domain
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Video-based emotion recognition in the wild
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Analyzing Group-Level Emotion with Global Alignment Kernel based Approach
Huang, X., Dhall, A., Goecke, R., Pietikäinen, M., & Zhao, G. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2953664 · Cited by 15
Self-citationEmotiW 2023: Emotion Recognition in the Wild Challenge
Dhall, A., Singh, M., Goecke, R., Gedeon, T., Zeng, D., Wang, Y., & Ikeda, K. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3616545 · Cited by 18
Self-citationEmotion recognition with multimodal features and temporal models
Wang, S., Wang, W., Zhao, J., Chen, S., Jin, Q., Zhang, S., & Qin, Y. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3143016 · Cited by 14
Group Emotion Recognition in Adverse Face Detection
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Automatic Prediction of Group Cohesiveness in Images
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Self-citationAn Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wildPreprint
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Facial Emotion Recognition in Smart Education Systems: A Review
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Metadata
- Authors
- Dhall, A., Goecke, R., Ghosh, S., Joshi, J., Hoey, J., & Gedeon, T.
- Affiliations
- Indian Institute of Technology Ropar (IN)
- University of Canberra (AU)
- University of Waterloo (CA)
- Australian National University (AU)
- Venue
- Proceedings of the 19th ACM International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2017-11-03
- Pages
- 524-528
- OpenAlex
- W2767348466
- Cited by
- 218
- References
- 23
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 11 · 2023: 20 · 2022: 23 · 2021: 23 · 2020: 34 · 2019: 41 · 2018: 33 · 2017: 13
- FWCI
- 38.12
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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 › Face and Expression Recognition
- Keywords
- affective computing, benchmarking platform, in-the-wild evaluation
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