Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face Recognition

Traditional linear Fukunaga-Koontz transform (FKT) (F. Fukunaga and W. Koontz, 1970) is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed kernel Fukunaga-Koontz transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.