Fisher linear discriminant analysis(LDA)

a well-known feature extraction method

searches for the projection axes on which the data samples from different classes are far from each other while requiring data samples of the same class to be close to each other.Large margin classifier(LMC)

also referred as linear support vector machine

de finds a project direction onto which two classes of the samples projected reach maximal margin.With combination of advantages of both LDA and LMC

the paper develops a novel linear projection classfication algorithm

called Fisher large margin linear classifier.The underlying idea is that an optimal discrimiant vector wbest is found along which the samples of high dimensional input space are projected such that the margin is maximized while within-class scatter is kept as small as possible.In addition

relations to other classifiers are explored in theory in this paper.Finally

the proposed method is tested on ORL face database and FERET face database.The experimental results show that the proposed classifier outperforms other linear classifiers.