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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
Face detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248014 · Cited by 2,171
Supervised Descent Method and Its Applications to Face Alignment
Xiong, X., & la Torre, F. D. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.75 · Cited by 1,926
CENTRIST: A Visual Descriptor for Scene Categorization
Wu, J., & Rehg, J. M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.224 · Cited by 710
Collecting Large, Richly Annotated Facial-Expression Databases from Movies
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.26 · Cited by 658
Self-citationDeep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657 · cited 1×
“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
Yu, Z., & Zhang, C. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830595 · Cited by 602
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130508 · Cited by 541 · cited 1×
“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
Self-citationCombining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild
Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., & Chen, X. (2014) · Proceedings of the 16th International Conference on Multimodal Interaction · doi:10.1145/2663204.2666274 · Cited by 202
Emotion recognition in the wild challenge 2013
Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531739 · Cited by 178
Self-citationEmotiW 2016: video and group-level emotion recognition challenges
Dhall, A., Goecke, R., Joshi, J., Hoey, J., & Gedeon, T. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997638 · Cited by 148
Self-citationKim, B.-K., Lee, H., Roh, J., & Lee, S.-Y. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830590 · Cited by 126
Automatic Group Happiness Intensity Analysis
Dhall, A., Goecke, R., & Gedeon, T. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2397456 · Cited by 117
Self-citationHernandez, J., Hoque, M. E., Drevo, W., & Picard, R. W. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370264 · Cited by 99
The more the merrier: Analysing the affect of a group of people in images
Dhall, A., Joshi, J., Sikka, K., Goecke, R., & Sebe, N. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163151 · Cited by 93
Self-citationFinding Happiest Moments in a Social Context
Dhall, A., Joshi, J., Radwan, I., & Goecke, R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37444-9_48 · Cited by 50
Self-citationGroup-level arousal and valence recognition in static images: Face, body and context
Mou, W., Çeliktutan, O., & Güneş, H. (2015) · IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7284862 · Cited by 49
Happiness level prediction with sequential inputs via multiple regressions
Li, J., Roy, S., Feng, J., & Sim, T. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997636 · Cited by 47
Group happiness assessment using geometric features and dataset balancing
Vonikakis, V., Yazıcı, Y., Nguyễn, V. D., & WINKLER, S. S. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997633 · Cited by 45
Multi-clue fusion for emotion recognition in the wild
Yan, J., Zheng, W., Cui, Z., Tang, C., Zhang, T., Zong, Y., & Sun, N. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997630 · Cited by 42
Huang, X., Dhall, A., Zhao, G., Goecke, R., & Pietikäinen, M. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.34 · Cited by 38
Self-citation
Flagged first, then by citations
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.
Cited by
Sorted by citations
Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
Facial Expression Analysis under Partial Occlusion
Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V. (2018) · ACM Computing Surveys · doi:10.1145/3158369 · Cited by 1,338
Survey on Emotional Body Gesture Recognition
Noroozi, F., Corneanu, C. A., Kamińska, D., Sapiński, T., Escalera, S., & Anbarjafari, G. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2874986 · Cited by 514
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Kollias, D., Tzirakis, P., Nicolaou, M. A., Papaioannou, A., Zhao, G., Schuller, B. W., Kotsia, I., & Zafeiriou, S. P. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01158-4 · Cited by 406
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
