A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition
Xiao‐Jun Wu, J. Kittler, J.-Y. Yang, K. Messer, S. T. Wang · 2004
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. 1.