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dc.contributor.authorMohamed Rizon
dc.contributor.authorMuhammad Firdaus, Hashim
dc.contributor.authorPuteh, Saad
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorMohd Rozailan, Mamat
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.
dc.contributor.authorAbdul Rahman, Saad
dc.contributor.authorHazri, Desa, Dr.
dc.contributor.authorKarthigayan, M.
dc.date.accessioned2011-03-23T06:36:18Z
dc.date.available2011-03-23T06:36:18Z
dc.date.issued2006
dc.identifier.citationAmerican Journal of Applied Sciences, vol.2(6), 2006, pages 1872-1875en_US
dc.identifier.issn1546-9239
dc.identifier.urihttp://www.scipub.org/fulltext/ajas/ajas361872-1875.pdf
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/11390
dc.descriptionLink to publisher's homepage at http://www.scipub.org/en_US
dc.description.abstractIn this study, we develop a computational model to identify the face of an unknown person’s by applying eigenfaces. The eigenfaces has been applied to extract the basic face of the human face images. The eigenfaces is then projecting onto human faces to identify unique features vectors. This significant features vector can be used to identify an unknown face by using the backpropagation neural network that utilized euclidean distance for classification and recognition. The ORL database for this investigation consists of 40 people with various 400 face images had been used for the learning. The eigenfaces including implemented Jacobi’s method for eigenvalues and eigenvectors has been performed. The classification and recognition using backpropagation neural network showed impressive positive result to classify face images.en_US
dc.language.isoenen_US
dc.publisherScience Publicationsen_US
dc.subjectFeature vectoren_US
dc.subjectEigenfacesen_US
dc.subjectEigenvaluesen_US
dc.subjectEigenvectoren_US
dc.titleFace recognition using eigenfaces and neural networksen_US
dc.typeArticleen_US


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