Face Recognition with Kernel Direct Discriminant Analysis and SVM Combined Method

Applications such as Face Recognition (FR) that deal with high-dimensional data need a mapping technique that introduces representation of lowdimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features .Most of traditiornl linear discriminant analysis (LDA) sufer from the disadvantage that their optimality criteria are not directly related to the classifcation ability of the obtained feature representation. Moreover, their classi"fication accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR laslrs. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine (SVM) classifier to deal with both of the shortcomings in an fficient and cost ffictive manner. The proposed here method is compared, in terms of classification accuracy, lo other commonly used FR methods on UMIST face datqbase. Resuhs indicate that the performance of the proposed method is overall superior to those oftraditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods and t r aditional line ar c lassifi ers.

Keywords:

Face Recognition , Kernel Direct Discriminant Analysis (KDDA) , small sample size problem (SSS) , Support Vector Machine (SVM)