E-2DLDA: A New Matrix-Based Image Representation Method for Face Recognition

Abstract

Two-dimensional linear discriminant analysis (2DLDA) was recently developed for face image representation and recognition by adopting the idea of image projection in 2DPCA. 2DLDA outperforms traditional LDA mainly in terms of feature extraction speed. Unfortunately, 2DLDA needs to use large numbers of features to represent an image sample, causing storage requirements are heavy and also feature matching process is time-consuming. Against this problem, we discuss in this paper a new image representation scheme called Enhanced 2DLDA (E-2DLDA) for face recognition. The main strategy adopted in our method is that two image projections are applied to an image sample jointly, so the dimensions of extracted feature matrix along both horizontal direction and vertical direction get compressed, and finally the total number of features can be reduced to a great extent. The experimental results on ORL database show that this method remarkably outperforms existing 2DLDA in terms of speed of feature matching and storage requirements of features.

This work was partially supported by the Program of 985 Innovation Engineering on Information in Xiamen University (2004-2007).