Face recognition using recursive Fisher linear discriminant

The 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 for calculating the discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.