Fusing face and body gesture for machine recognition of emotions

Gunes, H; Piccardi, M

Fusing face and body gesture for machine recognition of emotions

Gunes, H Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 2005, 2005 pp. 306 - 311
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Gunes, H	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 2005, 2005 pp. 306 - 311	en_US
dc.identifier.isbn	0780392752	en_US
dc.identifier.isbn	9780780392755	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2408
dc.description.abstract	Research shows that humans are more likely to consider computers to be human-like when those computers understand and display appropriate nonverbal communicative behavior. Most of the existing systems attempting to analyze the human nonverbal behavior focus only on the face; research that aims to integrate gesture as an expression mean has only recently emerged. This paper presents an approach to automatic visual recognition of expressive face and upper body action units (FAUs and BAUs) suitable for use in a vision-based affective multimodal framework. After describing the feature extraction techniques, classification results from three subjects are presented. Firstly, individual classifiers are trained separately with face and body features for classification into FAU and BAU categories. Secondly, the same procedure is applied for classification into labeled emotion categories. Finally, we fuse face and body information for classification into combined emotion categories. In our experiments, the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual face modality. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Workshop on Robot and Human Interactive Communication	en_US
dc.relation.isbasedon	10.1109/ROMAN.2005.1513796	en_US
dc.title	Fusing face and body gesture for machine recognition of emotions	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2005	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080602 Computer-Human Interaction	en_US
dc.location.activity	Nashville, USA	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
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2005	en_US

Abstract:

Research shows that humans are more likely to consider computers to be human-like when those computers understand and display appropriate nonverbal communicative behavior. Most of the existing systems attempting to analyze the human nonverbal behavior focus only on the face; research that aims to integrate gesture as an expression mean has only recently emerged. This paper presents an approach to automatic visual recognition of expressive face and upper body action units (FAUs and BAUs) suitable for use in a vision-based affective multimodal framework. After describing the feature extraction techniques, classification results from three subjects are presented. Firstly, individual classifiers are trained separately with face and body features for classification into FAU and BAU categories. Secondly, the same procedure is applied for classification into labeled emotion categories. Finally, we fuse face and body information for classification into combined emotion categories. In our experiments, the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual face modality. © 2005 IEEE.

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http://hdl.handle.net/10453/2408