Recognizing Faces using Kernel Eigenfaces and Support Vector Machines. - Naval, Prospero C.
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 CPA. 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