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dc.contributor.authorZhang, Ligang
dc.contributor.authorTjondronegoro, Dian
dc.contributor.editorLeung, CS
dc.contributor.editorLee, M
dc.contributor.editorChan, JH
dc.date.accessioned2020-01-14T06:29:07Z
dc.date.available2020-01-14T06:29:07Z
dc.date.issued2009
dc.identifier.isbn978-3-642-10676-7en_US
dc.identifier.issn0302-9743en_US
dc.identifier.doi10.1007/978-3-642-10677-4_83en_US
dc.identifier.urihttp://hdl.handle.net/10072/390259
dc.description.abstractThis paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing 'salient' Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using 'salient' Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.en_US
dc.languageEnglishen_US
dc.publisherSpringer-Verlag Berlin Heidelbergen_US
dc.relation.ispartofconferencename16th International Conference on Neural Information Processing (ICONIP 2009)en_US
dc.relation.ispartofconferencetitleLecture Notes in Computer Scienceen_US
dc.relation.ispartofdatefrom2009-12-01
dc.relation.ispartofdateto2009-12-05
dc.relation.ispartoflocationBangkok, Thailanden_US
dc.relation.ispartofpagefrom724en_US
dc.relation.ispartofpageto732en_US
dc.relation.ispartofissuePART 1en_US
dc.relation.ispartofvolume5863en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsComputer Science, Artificial Intelligenceen_US
dc.subject.keywordsComputer Science, Theory & Methodsen_US
dc.titleSelecting, Optimizing and Fusing 'Salient' Gabor Features for Facial Expression Recognitionen_US
dc.typeConference outputen_US
dcterms.bibliographicCitationZhang, L; Tjondronegoro, D, Selecting, Optimizing and Fusing 'Salient' Gabor Features for Facial Expression Recognition, Lecture Notes in Computer Science , 2009, 5863 (PART 1), pp. 724-732en_US
dc.date.updated2020-01-14T06:26:19Z
dc.description.versionPost-printen_US
gro.rights.copyright© 2009 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.comen_US
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.


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