BMVC 2004, Kingston, 7th-9th Sept, 2004
A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition
X. J. Wu (Jiangsu University of Science and Technology, China), J.
Kittler (University of Surrey), J.Y. Yang (Nanjing University of Science
& Technology, China), K. Messer (University of Surrey) and S. T. Wang
(Nanjing University of Science & Technology, China)
We propose a new kernel direct discriminant analysis (KDDA) algorithm in this paper. First, a recently
advocated direct linear discriminant analysis (DLDA) algorithm is overviewed. Then the new KDDA
algorithm is developed which can be considered as a kernel version of the DLDA algorithm. The
design of the minimum distance classifier in the new kernel subspace is then discussed. The results of
experiments on two well-known facial databases show the effectiveness of the proposed method in face
recognition. The results of experiments also confirm that DLDA can be viewed as a special case of the
proposed KDDA algorithm.