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dc.contributor.authorKamencay, P.
dc.contributor.authorZachariasova, M.
dc.contributor.authorHudec, R.
dc.contributor.authorJarina, R.
dc.contributor.authorBenco, M.
dc.contributor.authorHlubik, J.
dc.date.accessioned2015-01-20T14:14:14Z
dc.date.available2015-01-20T14:14:14Z
dc.date.issued2013-04cs
dc.identifier.citationRadioengineering. 2013, vol. 22, č. 1, s. 92-99. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/36804
dc.description.abstractIn this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods.en
dc.formattextcs
dc.format.extent92-99cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2013/13_01_0092_0099.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectImage segmentationen
dc.subjectface recognitionen
dc.subjectPCAen
dc.subjectKNNen
dc.subjectSPCA-KNNen
dc.subjectESSEX face database.en
dc.titleA Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Methoden
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue1cs
dc.coverage.volume22cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License