Feature extraction for pattern recognition

Abstract

Fisher Linear Discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems than traditional principal component analysis (PCA). However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure of calculating the discriminant features is suggested in this thesis. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with both Yale and ORL Databases, in which the resulting improvement of the performances by the new feature extraction scheme is significant.