Automatic visual recognition of face and body action units

Gunes, H; Piccardi, M

Automatic visual recognition of face and body action units

Gunes, H Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005, 2005, I pp. 668 - 673
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 - 3rd International Conference on Information Technology and Applications, ICITA 2005, 2005, I pp. 668 - 673	en_US
dc.identifier.isbn	0769523161	en_US
dc.identifier.isbn	9780769523163	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2418
dc.description.abstract	Expressive face and body gestures are among the main non-verbal communication channels in humanhuman interaction. Understanding human emotions through these nonverbal means is one of the necessary skills both for humans and also for the computers to interact intelligently and effectively with their human counterparts. Much progress has been achieved in affect assessment using a single measure type; however, reliable assessment typically requires the concurrent use of multiple modalities. Accordingly in this paper, we present preliminary results of automatic visual recognition of expressive face and upper-body action units (FAUs and BAUs) suitable for use in a vision-based affective multimodal framework. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005	en_US
dc.relation.isbasedon	10.1109/ICITA.2005.83	en_US
dc.title	Automatic visual recognition of face and body action units	en_US
dc.type	Conference Proceeding
utslib.citation.volume	I	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, Australia	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	closed_access
pubs.publication-status	Published	en_US
pubs.volume	I	en_US

Abstract:

Expressive face and body gestures are among the main non-verbal communication channels in humanhuman interaction. Understanding human emotions through these nonverbal means is one of the necessary skills both for humans and also for the computers to interact intelligently and effectively with their human counterparts. Much progress has been achieved in affect assessment using a single measure type; however, reliable assessment typically requires the concurrent use of multiple modalities. Accordingly in this paper, we present preliminary results of automatic visual recognition of expressive face and upper-body action units (FAUs and BAUs) suitable for use in a vision-based affective multimodal framework. © 2005 IEEE.

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http://hdl.handle.net/10453/2418