The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
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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Citations
Cites
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Self-citationDeep Learning for Emotion Recognition on Small Datasets using Transfer Learning
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“Researchers also proposed transfer learning based methods for dealing with smaller sized training data [17].”
BackgroundImage based Static Facial Expression Recognition with Multiple Deep Network Learning
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“A total of 22 teams participated in the new sub-challenge and the Static Facial Expressions in the Wild (SFEW) 2.0 database [5] was used.”
BackgroundInfluentialSelf-citationCombining modality specific deep neural networks for emotion recognition in video
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531745 · Cited by 391
Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015
Dhall, A., Ramana Murthy, O. V., Goecke, R., Joshi, J., & Gedeon, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2829994 · Cited by 305
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Research works printed but not linked: 2
- Abhinav Dhall , Roland Goecke , Jyoti Joshi , Karan Sikka , and Tom Gedeon . 2014 . Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol. From Individual to Group-Level Emotion Recognition: EmotiW.. ICMI’17, November 13–17, 2017, Glasgow , UK In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI). Abhinav Dhall, Roland Goecke, Jyoti Joshi, Karan Sikka, and Tom Gedeon. 2014. Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol. From Individual to Group-Level Emotion Recognition: EmotiW.. ICMI’17, November 13–17, 2017, Glasgow, UK In Proceedings of the ACM on International Conference on Multimodal Interaction (ICMI).
- Samira Ebrahimi , Chris Pal , Xavier Bouthillier , Pierre Froumenty , SÃľbastien Jean , Kishore Reddy Konda , Pascal Vincent, Aaron Courville, and Yoshua Bengio. 2013 . Samira Ebrahimi, Chris Pal, Xavier Bouthillier, Pierre Froumenty, SÃľbastien Jean, Kishore Reddy Konda, Pascal Vincent, Aaron Courville, and Yoshua Bengio. 2013.
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Facial Emotional Recognition Experiment by applying R-CNN
Nguyễn, H. Q. (2021) · UEF eRepo (University of Eastern Finland)
Emotion Recognition using Deep Learning Techniques and Word Embeddings
Kardakis, S., Perikos, I., Grivokostopoulou, F., & Paraskevas, M. (2021) · IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science (BCD) · doi:10.1109/bcd51206.2021.9581748
Automated recognition of facial expressions and gender in humans implemented on mobile devices
Moraru, R.-C., & Caţaron, A. (2021) · International Aegean Conference on Electrical Machines and Power Electronics & International Conference on Optimization of Electrical and Electronic Equipment (ACEMP-OPTIM) · doi:10.1109/optim-acemp50812.2021.9590033
An Experimentation to Measure the Influence of Music on Emotions
Generosi, A., Caresana, F., Dourou, N. A., Bruschi, V., Cecchi, S., & Mengoni, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35908-8_11
Dep-Emotion: Suppressing Uncertainty to Recognize Real Emotions in Depressed Patients
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HPGER: Integrating Human Perception Into Group Emotion Recognition
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Zhang, T., Dahlan, H. A., Xie, Z., Jin-feng, W., Chen, Y., Pan, Q., & Huang, Y. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-2156
SonicFER: Facial Expressions Tracking Through a Commercial Smartphone Speaker
Hao, Z., Yang, Z., Wang, Y., Wang, G., Li, M., & Cui, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3621431
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GEMS: Group Emotion Profiling Through Multimodal Situational Understanding
Kataria, A., Madan, S., Ghosh, S., Gedeon, T., & Dhall, A. (2025) · Machine learning for signal processing ... · doi:10.1109/mlsp62443.2025.11204342
Self-citationEmotion estimation from video footage with LSTM
Attrah, S. (2026) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1678984 · Cited by 1
Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels
Vielzeuf, V. (2019) · theses.fr (ABES) · doi:10.70675/cd4ec871zb372z492fz94bfz25086c5fda4a
Automated analysis of cohesion in small groups interactions
Maman, L. (2022) · theses.fr (ABES) · doi:10.70675/ddfa972fzdcd8z4734z8739z400b10805c9a
Vibe360+: An extended study on group-level deep emotional understanding in immersive communication
Colom, S. C., González-Sosa, E., Orduna, M., Amor-Martín, A., Pérez, P., & Villegas, Á. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117671
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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