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dc.contributor.authorZhang, Ligang
dc.contributor.authorTjondronegoro, Dian
dc.contributor.authorChandran, Vinod
dc.date.accessioned2020-01-14T05:59:58Z
dc.date.available2020-01-14T05:59:58Z
dc.date.issued2014
dc.identifier.issn0262-8856
dc.identifier.doi10.1016/j.imavis.2014.09.005
dc.identifier.urihttp://hdl.handle.net/10072/390253
dc.description.abstractRepresentation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal-valence (AV) dimensional space. The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom1067
dc.relation.ispartofpageto1079
dc.relation.ispartofissue12
dc.relation.ispartofjournalImage and Vision Computing
dc.relation.ispartofvolume32
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0906
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science, Software Engineering
dc.titleRepresentation of facial expression categories in continuous arousal-valence space: Feature and correlation
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZhang, L; Tjondronegoro, D; Chandran, V, Representation of facial expression categories in continuous arousal-valence space: Feature and correlation, Image and Vision Computing, 2014, 32 (12), pp. 1067-1079
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-01-14T05:56:57Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.


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