Automatic, Dimensional and Continuous Emotion Recognition

Gunes, H; Pantic, M

Automatic, Dimensional and Continuous Emotion Recognition

Gunes, H Pantic, M

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Publisher:: IGI Global
Publication Type:: Journal Article
Citation:: International Journal of Synthetic Emotions, 2010, 1 (1), pp. 68 - 99
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Gunes, H	en_US
dc.contributor.author	Pantic, M	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	International Journal of Synthetic Emotions, 2010, 1 (1), pp. 68 - 99	en_US
dc.identifier.issn	1947-9093	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15170
dc.description.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 modelling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.	en_US
dc.publisher	IGI Global	en_US
dc.relation.ispartof	International Journal of Synthetic Emotions	en_US
dc.relation.isbasedon	10.1109/FG.2013.6553785	en_US
dc.title	Automatic, Dimensional and Continuous Emotion Recognition	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	1	en_US

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 modelling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/15170