A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition | Zendy

XiaoJun Wu | Zendy; J. Kittler | Zendy; J.-Y. Yang | Zendy; K. Messer | Zendy; S. T. Wang | Zendy

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A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition

Author(s) -

XiaoJun Wu,

J. Kittler,

J.-Y. Yang,

K. Messer,

S. T. Wang

Publication year - 2004

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.18.54

Subject(s) - kernel fisher discriminant analysis , linear discriminant analysis , computer science , pattern recognition (psychology) , artificial intelligence , kernel (algebra) , facial recognition system , discriminant , face (sociological concept) , algorithm , mathematics , social science , combinatorics , sociology

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.

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