Two dimensional (2D) subspace classifiers for image recognition

Abstract

The Class-Featuring Information Compression (CLAFIC) is a pattern classification method which uses a linear subspace for each class. In order to apply the CLAFIC method to image recognition problems, 2D image matrices must be transformed into 1D vectors. In this paper, we propose new subspace classifiers to apply the conventional CLAFIC method directly to the image matrices. The proposed methods yield easier evaluation of correlation and covariance matrices, which in turn speeds up the training and testing phases. Moreover, experimental results on the AR and the ORL face databases also show that recognition performances of the proposed methods are typically better than recognition performances of other subspace classifiers given in the paper.