Feature Extraction Using Recursive Cluster-Based Linear Discriminant with Application to Face Recognition

Two new recursive procedures for extracting discriminant features, termed recursive modified linear discriminant (RMLD) and recursive cluster-based linear discriminant (RCLD) are proposed in this paper. The two new methods, RMLD and RCLD overcome two major shortcomings of Fisher linear discriminant (FLD): it can fully exploit all information available for discrimination; it removes the constraint on the total number of features that can be extracted. Extensive experiments of comparing the new algorithm with the traditional FLD and some of its variations, LDA based on null space of S_W, modified FLD (MFLD), and recursive FLD (RFLD), have been carried out on various types of face recognition problems for both Yale and JAFFE databases, in which the resulting improvement of the performances by the new feature extraction scheme is significant