Abstract :
A novel recursive procedure for extracting discriminant features, termed recursive cluster-based linear discriminant (RCLD), is proposed in this paper. Compared to the traditional Fisher linear discriminant (FLD) and its variations, RCLD has a number of advantages. First of all, it relaxes the constraint on the total number of features that can be extracted. Second, it fully exploits all information available for discrimination. In addition, RCLD is able to cope with multimodal distributions, which overcomes an inherent problem of conventional FLDs, which assumes uni-modal class distributions. Extensive experiments have been carried out on various types of face recognition problems for Yale, Olivetti Research Laboratory, and JAFFE databases to evaluate and compare the performance of the proposed algorithm with other feature extraction methods. The resulting improvement of performances by the new feature extraction scheme is significant
Keywords :
face recognition; feature extraction; recursive functions; visual databases; JAFFE databases; Olivetti Research Laboratory; RCLD; Yale; face recognition; feature extraction; recursive cluster-based linear discriminant; Clustering algorithms; Cross layer design; Data mining; Face recognition; Feature extraction; Laboratories; Principal component analysis; Scattering; Spatial databases; Vectors; Cluster-based linear discriminant (CLD); Fisher linear discriminant (FLD); face recognition; feature extraction; principal component analysis (PCA); recursive Fisher linear discriminant (RFLD); recursive cluster-based linear discriminant (RCLD);
