Recognizing Faces using Kernel Eigenfaces and Support Vector Machines
نویسنده
چکیده
In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low dimensional non-linearly embedded manifolds from data, thus providing a more suitable representation for subsequent recognition by a classifier. We provide experimental evidence which show that Kernel PCA performs better than PCA on the ATT Face Dataset when both are used with a linear Support Vector Machine Classifier.
منابع مشابه
Face Recognition using Eigenfaces , PCA and Supprot Vector Machines
This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...
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