An extreme case of the generalized optimal discriminant transformation and its application to face recognition

Authors:
Xiao-Jun Wu;Jie-Ping Lu;Jing-Yu Yang;Shi-Tong Wang;Josef Kittler
Affiliations:
School of Information Engineering, Southern Yangtze University, Wuxi 214122, PR China and School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, PR ...;School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China;School of Information, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Information Engineering, Southern Yangtze University, Wuxi 214122, PR China;Centre for Vision, Speech and Signal Processing, University of Surrey,GU2 7XH, Surrey, UK
Venue:
Neurocomputing
Year:
2007

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Abstract

A study has been made on an extreme case of generalized optimal set of discriminant vectors. Equivalence between the generalized K-L transformation and the generalized optimal discriminant transformation is proved under the condition that the population scatter matrix of training samples is nonsingular. A new algorithm for determining the generalized optimal set of discriminant vectors is proposed based on the above theory, which is applied to the feature extraction of human face images. The results of experiments conducted on ORL and Yale databases show the effectiveness of the new feature extraction algorithm based on the extreme case of the generalized optimal discriminant transformation.