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Kernel-based nonlinear discriminant analysis for face recognition

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Abstract

Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method — Fisher Linear Discriminant Analysis (FLDA). First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.

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Authors and Affiliations

  1. National Lab of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Liu QingShan, Huang Rui, Lu HanQing & Ma SongDe

Authors
  1. Liu QingShan
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  2. Huang Rui
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  3. Lu HanQing
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  4. Ma SongDe
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Corresponding author

Correspondence to Liu QingShan.

Additional information

This work is supported by the National High Technology Development 863 Program of China under Grant No.2001AA114140 and the National Natural Science Foundation of China under Grant No.60135020.

LIU QingShan was born in 1975. He received his B.E. degree from Chinese Textile University (now DongHua University) in 1997, M.S. degree from Southeast University in 2000. He received his Ph.D. degree in 2003 from the National Laboratory of Pattern Recognition of Institute of Automation, the Chinese Academy of Sciences, where he is an assistant professor. His current research interests are image and video processing, object tracking and recognition, and machine learning.

HUANG Rui received his B.E. degree from Peking University in 1999, and M.S. degree from the National Laboratory of Pattern Recognition, the Chinese Academy of Sciences. Now he studies in USA, and his current research interests are motion analysis, graph model and pattern recognition.

LU HanQing Was born in 1961. He received his B.E. degree and M.S. degree both from Harbin Institute of Technology in 1982 and 1985 respectively. He received his Ph.D. degree from Huazhong University of Science and Technology in 1992. Since 1992, he has been with the Institute of Automation of the Chinese Academy of Sciences, where he is now a professor His research interests include image processing, content based image and video retrieval, object tracking and recognition.

MA SongDe was born in 1946. He received the B.S. degree from Tsinghua University in 1968, the Ph.D. degree in 1983 and the “Doctorat d’Etat es Science’ degree in 1986 from University of Paris 6. He was an invited researcher in the Computer Vision Laboratory of University of Maryland, College Park, U.S.A. in 1983. He was a researcher in the robot vision laboratory in INRIA, France in 1984–1986. Since 1986, he has been a research professor in the National Pattern Recognition Laboratory of the Institute of Automation, the Chinese Academy of Sciences. He was the president of the Institute of Automation of the Chinese Academy f Sciences in 1996–2000. He has been the vice-minister of the Ministry of Science and Technology (MOST) of China since April 2000. He is a senior member of IEEE. His research interests include computer vision, computer graphics, robotics, neural computing.

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Liu, Q., Huang, R., Lu, H. et al. Kernel-based nonlinear discriminant analysis for face recognition. J. Comput. Sci. & Technol. 18, 788–795 (2003). https://doi.org/10.1007/BF02945468

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  • DOI: https://doi.org/10.1007/BF02945468

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