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dc.contributor.authorShenoy, A.
dc.contributor.authorAnthony, S.
dc.contributor.authorFrank, R.
dc.contributor.authorDavey, N.
dc.date.accessioned2013-01-14T14:59:18Z
dc.date.available2013-01-14T14:59:18Z
dc.date.issued2009
dc.identifier.citationShenoy , A , Anthony , S , Frank , R & Davey , N 2009 , Discriminating angry, happy and neutral facial expression : a comparison of computational models . in Communications in Computer and Information Science : Engineering Applications of Neural Networks, Proceedings . vol. 43 , Springer , pp. 200-209 . https://doi.org/10.1007/978-3-642-03969-0_19
dc.identifier.isbn978-3-642-03969-0
dc.identifier.isbn978-3-642-03968-3
dc.identifier.otherPURE: 99639
dc.identifier.otherPURE UUID: 79f53335-75fc-45a5-a47b-a93a540368f5
dc.identifier.otherdspace: 2299/5803
dc.identifier.otherScopus: 78049370531
dc.identifier.urihttp://hdl.handle.net/2299/9621
dc.description“The original publication is available at www.springerlink.com” Copyright Springer
dc.description.abstractRecognizing expressions are a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high dimensional data, so here we use two dimensionality reduction techniques: Principal Component Analysis and Curvilinear Component Analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a happy expression and neutral expression from those of angry expressions and neutral expression with better accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components with happy faces and 5 components with angry faces.en
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofCommunications in Computer and Information Science
dc.subjectfacial expressions
dc.subjectimage analysis
dc.subjectclassification
dc.subjectdimensionality reduction
dc.titleDiscriminating angry, happy and neutral facial expression : a comparison of computational modelsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=78049370531&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2009
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-642-03969-0_19
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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