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dc.contributor.authorPan, Z.
dc.contributor.authorRust, A.G.
dc.contributor.authorBolouri, H.
dc.date.accessioned2011-11-22T15:01:04Z
dc.date.available2011-11-22T15:01:04Z
dc.date.issued2000
dc.identifier.citationPan , Z , Rust , A G & Bolouri , H 2000 , Image redundancy reduction for neural network classification using discrete cosine transforms . in Procs of IEEE-INNS-ENNS Int Jt Conf on Neural Networks : IJCNN 2000 . vol. 3 , IEEE , pp. 149-154 , IEEE-INNS-ENNS Int Joint Conf on Neural Networks , Como , Italy , 24/07/00 . https://doi.org/10.1109/IJCNN.2000.861296
dc.identifier.citationconference
dc.identifier.isbn0-7695-0619-4
dc.identifier.otherPURE: 457862
dc.identifier.otherPURE UUID: d33d66d7-1c7e-448c-8e1c-22e7cd706c24
dc.identifier.otherdspace: 2299/3979
dc.identifier.otherScopus: 0033683813
dc.identifier.urihttp://hdl.handle.net/2299/7089
dc.description“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/IJCNN.2000.861296
dc.description.abstractHigh information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features, such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, high recognition rates can be achieved using only a small proportion (0.19%) of available transform components. This makes DCT-based face recognition more than two orders of magnitude faster than other approaches.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of IEEE-INNS-ENNS Int Jt Conf on Neural Networks
dc.rightsOpen
dc.subjectbackpropagation
dc.subjectface recognition
dc.titleImage redundancy reduction for neural network classification using discrete cosine transformsen
dc.contributor.institutionSchool of Computer Science
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2000
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/IJCNN.2000.861296
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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