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dc.contributor.authorTurhal, Ümit Çiğdem
dc.contributor.authorDuysak, Alpaslan
dc.date.accessioned30.04.201910:49:13
dc.date.accessioned2019-07-13T16:27:37Z
dc.date.available30.04.201910:49:13
dc.date.available2019-07-13T16:27:37Z
dc.date.issued2011
dc.identifier.issn1300-0632
dc.identifier.urihttps://trdizin.gov.tr/publication/paper/detail/TVRJd01qQTJOZz09
dc.identifier.urihttps://hdl.handle.net/20.500.12438/1122
dc.description.abstractIn this paper, a new algorithm using 2DPCA and Gram-Schmidt Orthogonalization Procedure for recognition of face images is proposed. The algorithm consists of two parts. In the first part, a common feature matrix is obtained; and in the second part, the dimension of the common feature matrix is reduced. Resulting common feature matrix with reduced dimension is used for face recognition. Column and row covariance matrices are obtained by applying 2DPCA on the column and row vectors of images, respectively. The algorithm then applies eigenvalue-eigenvector decomposition to each of these two covariance matrices. Total scatter maximization is achieved taking the projection of images onto d eigenvectors corresponding to the largest d eigenvalues of column covariance matrix, yielding the feature matrix. The each column of the feature matrix represents a feature vector. Minimization of within class scatter is achieved by reducing the redundancy of the corresponding feature vectors of the different images in the same class. A common feature vector for each dth eigenvector direction is obtained by applying Gram-Schmidt Orthogonalization Procedure. A common feature matrix is established by gathering d common feature vectors in a matrix form. Then, the dimension of common feature matrix is reduced to d×d taking the projection of common feature matrix onto d eigenvectors which corresponds to the largest d eigenvalues of row covariance matrix. The performance of the proposed algorithm is evaluated experimentally by measuring the recognition rates. The developed algorithm produced better recognition rates compared to Eigenface, Fisherface and 2DPCA methods. Ar-Face and ORL face databases are used in the experimental evaluations.en_US
dc.description.abstractIn this paper, a new algorithm using 2DPCA and Gram-Schmidt Orthogonalization Procedure for recognition of face images is proposed. The algorithm consists of two parts. In the first part, a common feature matrix is obtained; and in the second part, the dimension of the common feature matrix is reduced. Resulting common feature matrix with reduced dimension is used for face recognition. Column and row covariance matrices are obtained by applying 2DPCA on the column and row vectors of images, respectively. The algorithm then applies eigenvalue-eigenvector decomposition to each of these two covariance matrices. Total scatter maximization is achieved taking the projection of images onto d eigenvectors corresponding to the largest d eigenvalues of column covariance matrix, yielding the feature matrix. The each column of the feature matrix represents a feature vector. Minimization of within class scatter is achieved by reducing the redundancy of the corresponding feature vectors of the different images in the same class. A common feature vector for each dth eigenvector direction is obtained by applying Gram-Schmidt Orthogonalization Procedure. A common feature matrix is established by gathering d common feature vectors in a matrix form. Then, the dimension of common feature matrix is reduced to d×d taking the projection of common feature matrix onto d eigenvectors which corresponds to the largest d eigenvalues of row covariance matrix. The performance of the proposed algorithm is evaluated experimentally by measuring the recognition rates. The developed algorithm produced better recognition rates compared to Eigenface, Fisherface and 2DPCA methods. Ar-Face and ORL face databases are used in the experimental evaluations.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMühendisliken_US
dc.subjectElektrik ve Elektroniken_US
dc.subject2DPCAen_us
dc.subjectGram-Schmidt Orthogonalizationen_us
dc.subjectCommon Feature Matrixen_us
dc.subjectFace Recognitionen_us
dc.subjectTotal Scatter Maximizationen_us
dc.subjectWithin-Class Scatter Minimizationen_us
dc.titleAn algorithm to minimize within-class scatter and to reduce common matrix dimension for image recognitionen_US
dc.typearticleen_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.departmentMühendislik Fakültesien_US
dc.identifier.volume19en_US
dc.identifier.issue6en_US
dc.identifier.startpage929en_US
dc.identifier.endpage939en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US]


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