Optical flow based analyses to detect emotion from human facial image data

Besinger, A; Sztynda, T; Lal, S; Duthoit, C; Agbinya, J; Jap, B; Eager, D; Dissanayake, G

Optical flow based analyses to detect emotion from human facial image data

Besinger, A Sztynda, T

Lal, S

Duthoit, C Agbinya, J Jap, B Eager, D

Dissanayake, G

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2010, 37 (12), pp. 8897 - 8902
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Besinger, A	en_US
dc.contributor.author	Sztynda, T https://orcid.org/0000-0002-8957-0864	en_US
dc.contributor.author	Lal, S https://orcid.org/0000-0002-0911-0850	en_US
dc.contributor.author	Duthoit, C	en_US
dc.contributor.author	Agbinya, J	en_US
dc.contributor.author	Jap, B	en_US
dc.contributor.author	Eager, D https://orcid.org/0000-0003-1926-7867	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2010, 37 (12), pp. 8897 - 8902	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13596
dc.description.abstract	Artificial recognition of facial expression has attracted a lot of attention in the last few years and different facial expression detection methods have been developed. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes and mouth) to capture basic emotions. The used dataset contains a total 60 facial images from subject's different genders and nationality not wearing glasses and/or facial hair. Results show that the used point tracking algorithm separately applied to the five facial image regions can detect emotions in image sequences. © 2010 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2010.05.063	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Optical flow based analyses to detect emotion from human facial image data	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	37	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	Yeppoon, Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

Artificial recognition of facial expression has attracted a lot of attention in the last few years and different facial expression detection methods have been developed. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes and mouth) to capture basic emotions. The used dataset contains a total 60 facial images from subject's different genders and nationality not wearing glasses and/or facial hair. Results show that the used point tracking algorithm separately applied to the five facial image regions can detect emotions in image sequences. © 2010 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/13596