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dc.contributor.authorPantraki, Evangelia-
dc.contributor.authorKotropoulos, Constantine L.-
dc.contributor.authorLanitis, Andreas-
dc.date.accessioned2017-11-20T11:43:08Z-
dc.date.available2017-11-20T11:43:08Z-
dc.date.issued2017-07-01-
dc.identifier.citationIET Biometrics, 2017, vol. 6, no. 4, pp. 290-298en_US
dc.identifier.issn20474938-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/10536-
dc.description.abstractParallel factor analysis 2 (PARAFAC2) is employed to reduce the dimensions of visual and aural features and provide ranking vectors. Subsequently, score level fusion is performed by applying a support vector machine (SVM) classifier to the ranking vectors derived by PARAFAC2 to make gender and age interval predictions. The aforementioned procedure is applied to the Trinity College Dublin Speaker Ageing database, which is supplemented with face images of the speakers and two single-modality benchmark datasets. Experimental results demonstrate the advantage of using combined aural and visual features for both prediction tasks.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIET Biometricsen_US
dc.rights© The Institution of Engineering and Technologyen_US
dc.subjectPARAFAC2en_US
dc.subjectAutomatic predictionen_US
dc.subjectTCDSA databaseen_US
dc.titleAge interval and gender prediction using PARAFAC2 and SVMs based on visual and aural featuresen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationAristotle University of Thessalonikien_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1049/iet-bmt.2016.0122en_US
dc.relation.issue4en_US
dc.relation.volume6en_US
cut.common.academicyear2016-2017en_US
dc.identifier.spage290en_US
dc.identifier.epage298en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2047-4946-
crisitem.journal.publisherIEEE-
crisitem.author.deptDepartment of Multimedia and Graphic Arts-
crisitem.author.facultyFaculty of Fine and Applied Arts-
crisitem.author.orcid0000-0001-6841-8065-
crisitem.author.parentorgFaculty of Fine and Applied Arts-
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