2DPCA Feature Selection Using Mutual Information

Abstract

In two-dimensional principal component analysis (2DPCA), 2D face image matrices do not need to be previously transformed into a vector. In this way, the image covariance matrix can be better estimated, compared to the old fashion. The feature is derived from eigenvectors corresponding to the largest eigenvalues of the image covariance matrix for data of all classes. Normally, the number of the largest eigenvalues is selected manually for obtaining the optimal feature matrix. In this paper, we propose a novel method for feature selection in 2DPCA, based on mutual information concept for automatically selecting the number of the largest eigenvalues. The non-parametric quadratic mutual information between class labels and features is used as a selection criterion. This does not only allows reducing of the dimension of feature matrix but also obtaining the good recognition accuracy. Experimental results on Yale face database showed an efficient of our proposed method.