MULTIMODAL EMOTION RECOGNITION
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Probabilistic graphical models, such as Hidden Markov Models, Bayesian networks and Dynamic Bayesian networks, are very well suited for fusing different sources of information in multimodal emotion recognition and can handle noisy features and missing values by probabilistic inference.
“Sebe et al. [3] highlight that probabilistic graphical models, such as Hidden Markov Models, Bayesian networks and Dynamic Bayesian networks are very well suited for fusing different sources of information in multimodal emotion recognition and can also handle noisy features and missing values of features all by probabilistic inference.”
From Multimodal emotion recognition from expressive faces, body gestures and speech (2007) · cites this paper as [3]
The indexed copy contains an archive entry for the chapter, including PDF listings under Abstract, but no chapter-text passage addressing the claim. Searches for graphical models, probabilistic inference, noisy or missing features, and multimodal fusion returned only archive metadata, so the attributed statement cannot be verified from this copy. (LLM, conf=0.75)
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References 53 · Cited by 201
- Authors
- Sebe, N., Cohen, I., & Huang, T. S.
- Affiliations
- University of Amsterdam (NL)
- Hewlett-Packard (United States) (US)
- University of Illinois Urbana-Champaign (US)
- Venue
- Handbook of Pattern Recognition and Computer Vision
- Publisher
- WORLD SCIENTIFIC
- Published
- 2005-01
- Pages
- 387-409
- OpenAlex
- W1552007786
- Cited by
- 201
- References
- 53
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 18 · 2023: 20 · 2022: 29 · 2021: 9 · 2020: 6 · 2019: 8 · 2018: 11 · 2017: 5 · 2016: 7 · 2015: 16 · 2014: 10 · 2013: 11 · 2012: 10
- FWCI
- 18.50
- Type
- book chapter
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- WORLD SCIENTIFIC eBooks (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- multimodal emotion recognition, emotion recognition, multimodal fusion, facial expressions, speech emotion recognition
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