  Applications such as face recognition that deal with high-dimensional data
need a mapping technique that introduces representation of low-dimensional
features with enhanced discriminatory power and a proper classifier, able to
classify those complex features. Most of traditional Linear Discriminant
Analysis suffer from the disadvantage that their optimality criteria are not
directly related to the classification ability of the obtained feature
representation. Moreover, their classification accuracy is affected by the
"small sample size" problem which is often encountered in FR tasks. In this
short paper, we combine nonlinear kernel based mapping of data called KDDA with
Support Vector machine classifier to deal with both of the shortcomings in an
efficient and cost effective manner. The proposed here method is compared, in
terms of classification accuracy, to other commonly used FR methods on UMIST
face database. Results indicate that the performance of the proposed method is
overall superior to those of traditional FR approaches, such as the Eigenfaces,
Fisherfaces, and D-LDA methods and traditional linear classifiers.
