The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Automatic, Dimensional and Continuous Emotion Recognition
Abstract
Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition of prototypic expressions of six basic emotions based on data that has been posed on demand and acquired in laboratory settings. More recently, there has been a shift toward recognition of affective displays recorded in naturalistic settings as driven by real world applications. This shift in affective computing research is aimed toward subtle, continuous, and context-specific interpretations of affective displays recorded in real-world settings and toward combining multiple modalities for analysis and recognition of human emotion. Accordingly, this article explores recent advances in dimensional and continuous affect modeling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.
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Cites
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Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
An extended set of Haar-like features for rapid object detection
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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
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Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Multisensory perception: Beyond modularity and convergence
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An empirical study of machine learning techniques for affect recognition in human–robot interaction
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Machine Analysis of Facial Expressions
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Cited by
Memory Fusion Network for Multi-view Sequential Learning
Zadeh, A., Liang, P. P., Mazumder, N., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12021 · Cited by 855
McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665
Nicolaou, M. A., Gunes, H., & Pantic, M. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.9 · Cited by 450
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., & Schroeder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.27 · Cited by 381
Self-citationWavelet-based emotion recognition system using EEG signal
Mohammadi, Z., Frounchi, J., & Amiri, M. (2017) · Neural Computing and Applications · doi:10.1007/s00521-015-2149-8 · Cited by 361
Metadata
- Authors
- Gunes, H., & Pantic, M.
- Venue
- International Journal of Synthetic Emotions
- Publisher
- IGI Global
- Published
- 2010-01-01
- Volume
- 1
- Issue
- 1
- Pages
- 68-99
- ISSN
- 1947-9093 (ISSN-L)
- 1947-9107
- OpenAlex
- W2153822685
- Cited by
- 426
- References
- 11
- FWCI
- 36.76
- Type
- journal article
- Language
- ng
- Open access
- closed
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