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Full-Text Articles in Computer Engineering
Two-Class Classification With Various Characteristics Based On Kernel Principal Component Analysis And Support Vector Machines, Ivanna Kristianti Timotius, Iwan Setyawan, Andreas Ardian Febrianto
Two-Class Classification With Various Characteristics Based On Kernel Principal Component Analysis And Support Vector Machines, Ivanna Kristianti Timotius, Iwan Setyawan, Andreas Ardian Febrianto
Makara Journal of Technology
Two class pattern classification problems appeared in many applications. In some applications, the characteristic of the members in a class is dissimilar. This paper proposed a classification system for this problem. The proposed system was developed based on the combination of kernel principal component analysis (KPCA) and support vector machines (SVMs). This system has been implemented in a two class face recognition problem. The average of the classification rate in this face image classification is 82.5%.
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that …
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
Turkish Journal of Electrical Engineering and Computer Sciences
In 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 …