Abstract
A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper. This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time. A lot of computation time can be saved because all the generalized optimal sets of discriminant vectors can be obtained simultaneously with the proposed algorithm, while it needs no iterative operations. The proposed algorithm can yield a much higher recognition rate. Furthermore, the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only. These statements are supported by the numerical simulation experiments on facial database of ORL.
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This work was supported in part by the National Natural Science Foundation of China (Grant No.60072034), Foundation of Robotics Laboratory, The Chinese Academy of Sciences (Grant No.RL200108), and University's Natural Science Research Program of Jiangsu Province, P.R. China (Grant No.01KJB520002).
WU Xiaojun received his B.S. degree in mathematics from Nanjing Normal University, Nanjing, P.R. China in 1991 and M.S. degree in engineering from Nanjing University of Science and Technology, Nanjing, P.R. China in 1996 respectively. He is currently working toward the Ph.D. degree in the Department of Computers at the same university. He is also an associate professor in East China Shipbuilding Institute. Since 1996, he has been teaching in the Department of Electronics and Information, East China Shipbuilding Institute where he is an assistant dean of the Department and director of the Center of Computer Fundamental Education of East China Shipbuilding Institute. His current research interests are pattern recognition, fuzzy systems, neural networks and intelligent systems. He has published more than 30 papers in the above areas. Dr. Wu was a fellow of United Nations University, International Institute for Software Technology (UNU/IIST) from 1999 to 2000. he won the most outstanding postgraduate award from Nanjing University of Science and Technology in 1996, and outstanding paper award from the Committee of Military System Engineering, China System Engineering Society in 1999.
YANG Jingyu received his B.S. degree in computer science from Harbin Institute of Military Engineering, Harbin, China. From 1982 to 1984 he was a visiting scholar at the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign. From 1993 to 1994 he was a visiting professor at the Department of Computer Science, Missouri University. He was a visiting professor at Concordia University in Canada in 1998. He is currently a professor and Chairman in the Faculty of Information at Nanjing University of Science and Technology. He is the author of over 200 scientific papers in computer vision, pattern recognition, and artificial intelligence. He has won more than 20 provincial awards and national awards. His current research interests are in the areas of pattern recognition, robot vision, image processing, information fusion, and artificial intelligence.
WANG Shitong received his B.S. and M.S. degrees both in computer science from Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China in 1984 and 1987 respectively. He is a professor in East China Shipbuilding Institute, where he is the dean of the Department of Electronics and Information. His current research interests are pattern recognition, fuzzy systems, neural networks and intelligent systems. He has published more than 120 papers in the above areas. Prof. Wang was a visiting fellow in London University from 1995 to 1996. He was a postdoctoral fellow in Bristol University which belongs to Royal Society of Great Bratain from 1998 to 1999. Since the beginning of this year, he has been a visiting professor at Hong Kong University of Science and Technology.
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Wu, X., Jingyu, Y., Wang, S. et al. A new algorithm for generalized optimal discriminant vectors. J. of Comput. Sci. & Technol. 17, 324–330 (2002). https://doi.org/10.1007/BF02947310
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DOI: https://doi.org/10.1007/BF02947310