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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- 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
- 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 › Face and Expression Recognition
- Keywords
- affective computing, benchmarking platform, in-the-wild evaluation
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