Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/10536
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pantraki, Evangelia | - |
dc.contributor.author | Kotropoulos, Constantine L. | - |
dc.contributor.author | Lanitis, Andreas | - |
dc.date.accessioned | 2017-11-20T11:43:08Z | - |
dc.date.available | 2017-11-20T11:43:08Z | - |
dc.date.issued | 2017-07-01 | - |
dc.identifier.citation | IET Biometrics, 2017, vol. 6, no. 4, pp. 290-298 | en_US |
dc.identifier.issn | 20474938 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/10536 | - |
dc.description.abstract | Parallel 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.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IET Biometrics | en_US |
dc.rights | © The Institution of Engineering and Technology | en_US |
dc.subject | PARAFAC2 | en_US |
dc.subject | Automatic prediction | en_US |
dc.subject | TCDSA database | en_US |
dc.title | Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Aristotle University of Thessaloniki | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Cyprus | en_US |
dc.country | Greece | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1049/iet-bmt.2016.0122 | en_US |
dc.relation.issue | 4 | en_US |
dc.relation.volume | 6 | en_US |
cut.common.academicyear | 2016-2017 | en_US |
dc.identifier.spage | 290 | en_US |
dc.identifier.epage | 298 | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.grantfulltext | none | - |
crisitem.author.dept | Department of Multimedia and Graphic Arts | - |
crisitem.author.faculty | Faculty of Fine and Applied Arts | - |
crisitem.author.orcid | 0000-0001-6841-8065 | - |
crisitem.author.parentorg | Faculty of Fine and Applied Arts | - |
crisitem.journal.journalissn | 2047-4946 | - |
crisitem.journal.publisher | IEEE | - |
Appears in Collections: | Άρθρα/Articles |
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