Dimensionality Reduction for Semi-supervised Face Recognition

Abstract

A dimensionality reduction technique is presented for semi-supervised face recognition where image data are mapped into a low dimensional space with a spectral method. A mapping of learning data is generalized to a new datum which is classified in the low dimensional space with the nearest neighbor rule. The same generalization is also devised for regularized regression methods which work in the original space without dimensionality reduction. It is shown with experiments that the spectral mapping method outperforms the regularized regression. A modification scheme for data similarity matrices on the basis of label information and a simple selection rule for data to be labeled are also devised.