The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Affective multimodal human-computer interaction
Abstract
Social and emotional intelligence are aspects of human intelligence that have been argued to be better predictors than IQ for measuring aspects of success in life, especially in social interactions, learning, and adapting to what is important. When it comes to machines, not all of them will need such skills. Yet to have machines like computers, broadcast systems, and cars, capable of adapting to their users and of anticipating their wishes, endowing them with the ability to recognize user's affective states is necessary. This article discusses the components of human affect, how they might be integrated into computers, and how far are we from realizing affective multimodal human-computer interaction.
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In the citing paper
“Previous software primarily dealt with offline emotion recognition that cause post-processing of the learner's data.”
From Towards multimodal emotion recognition in e-learning environments (2016) · cites this paper as Pantic, Sebe, Cohn, & Huang, 2005
In the citing paper
“The application context is restricted by the fact that such software can only manage a small set of expressions from frontal view faces without facial hair, glasses provided that there is constant illumination.”
From Towards multimodal emotion recognition in e-learning environments (2016) · cites this paper as Pantic, Sebe, Cohn, & Huang, 2005
Citations
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Cited by
Sorted by citations
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Self-citationAffective Computing and Sentiment Analysis
Cambria, E. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.31 · Cited by 1,308
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
Multimodal human–computer interaction: A survey
Jaimes, A., & Sebe, N. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.019 · Cited by 965
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Metadata
- Authors
- Pantic, M., Sebe, N., Cohn, J. F., & Huang, T.
- Affiliations
- Delft University of Technology, The Netherlands
- University of Amsterdam (NL)
- University of Pittsburgh (US)
- University of Illinois Urbana-Champaign (US)
- Venue
- Proceedings of the 13th annual ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2005-11-06
- Pages
- 669-676
- OpenAlex
- W2028975938
- Cited by
- 234
- References
- 50
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 8 · 2023: 8 · 2022: 23 · 2021: 6 · 2020: 10 · 2019: 9 · 2018: 11 · 2017: 9 · 2016: 7 · 2015: 12 · 2014: 6 · 2013: 14 · 2012: 8
- FWCI
- 25.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- affective computing, multimodal human-computer interaction, affective states, social-emotional intelligence, user modeling, adaptive systems, emotion recognition
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