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Journal of Computer Science and Technology

, Volume 17, Issue 3, pp 324–330 | Cite as

A new algorithm for generalized optimal discriminant vectors

  • Wu Xiaojun Email author
  • Yang Jingyu
  • Wang Shitong 
  • Guo Yuefei
  • Cao Qiying 
Correspondence

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.

Keywords

pattern recognition feature extraction discriminant analysis generalized optimal set of discriminant vectors face recognition 

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References

  1. [1]
    Rama Chellappa, Wilson C L, Sirohey S. Human and machine recognition of faces: A survey. InProceedings of the IEEE, 1995, 83(5): 705–740.Google Scholar
  2. [2]
    Rosenfeld A. Survey: Image analysis and computer vision: 1996.Computer Vision and Image Understanding, 1997, 62(1): 33–93.CrossRefGoogle Scholar
  3. [3]
    Turk M, Pentland A. Eigenfaces for recognition.J. Cognitive Neuroscience, 1991, 3(1): 71–86.CrossRefGoogle Scholar
  4. [4]
    Swets D, Weng J, Using discriminant eigenfeatures for image retrieval.IEEE Trans. Pattern Analysis and Machine Intelligence, 1996, 18(8): 831–836.CrossRefGoogle Scholar
  5. [5]
    Moghaddam B, Pentland A. Probabilistic visual learning for object representation.IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, 19(7): 696–710.CrossRefGoogle Scholar
  6. [6]
    Foley D H, Sammon J W. An optimal set of discriminant vectors.IEEE Trans. Computer, 1975, 24(3): 281–289.zbMATHCrossRefGoogle Scholar
  7. [7]
    Fukunaga K. Introduction to Statistical Pattern Recognition. Academic Press, New York, 1990.zbMATHGoogle Scholar
  8. [8]
    Rowley H A, Baluja S, Kanade T. Neural network-based face detection.IEEE Trans. Pattern Analysis and Machine Intelligence, 1998, 20(1): 25–38.CrossRefGoogle Scholar
  9. [9]
    Yoon K Set al. Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network.Pattern Recognition, 1998, 31(3): 283–293.CrossRefGoogle Scholar
  10. [10]
    Sammon J W. An optimal discriminant plane.IEEE Trans. Computer, 1970, 19(3): 826–829.zbMATHCrossRefGoogle Scholar
  11. [11]
    Tian Qet al. Image classification by the Foley-Sammon transform.Optical Engineering, 1986, 25(7): 834–839.Google Scholar
  12. [12]
    Hong Z Q. Algebraic feature extraction of images for recognition.Pattern Recognition, 1991, 24(3): 211–219.CrossRefMathSciNetGoogle Scholar
  13. [13]
    Okada Tet al. Theory of feature extraction by orthogonal discriminant vectors.Trans. IECE (A), 1982, 65(8): 767–771. (in Japanese).Google Scholar
  14. [14]
    Hamamoto Yet al. A note on the orthogonal discriminant vector of pattern recognition.Trans. IECE (A), 1989, 72(2): 414–419. (in Japanese)Google Scholar
  15. [15]
    Kittler J. On the discriminant vector method of feature selection.IEEE Trans. Comput., 1977, 26(6): 604–606.CrossRefGoogle Scholar
  16. [16]
    Hong Z Q, Yang J Y. Optimal discriminant plane for a small number of samples and disign method of classifier on the plane.Pattern Recognition, 1991, 24(4): 317–324.CrossRefMathSciNetGoogle Scholar
  17. [17]
    Cheng Y Q, Zhuang Y M, Yang J Y. Optimal fisher discriminant analysis using the rank decomposition.Pattern Recognition, 1992, 25(1): 101–111.CrossRefMathSciNetGoogle Scholar
  18. [18]
    Liu K, Cheng Y Q, Yang J Y. An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraic method.International Journal of Pattern Recognition and Artificial Intelligence, 1992, 6(5): 817–829.CrossRefGoogle Scholar
  19. [19]
    Liu K, Cheng Y Q, Yang J Y. A generalized optimal set of discriminant vectors.Pattern Recognition, 1992, 25(1): 731–739.CrossRefGoogle Scholar
  20. [20]
    Wang Genglu, Shi Rongchang. Theory of Matrix. National Defense Industry Press, Beijing, 1988.Google Scholar
  21. [21]
    Sun Jiguang. Perturbation Analysis of Matrix. Science Press, Beijing, 1987.Google Scholar
  22. [22]
    Liu K, Cheng Y Q, Yang J Y. Algebraic feature extraction for image recognition based on an optimal discriminant criterion.Pattern Recognition, 1993, 26(1): 903–911.CrossRefGoogle Scholar
  23. [23]
    Guo Yuefei, Yang Jingyu. An iterative algorithm for the generalized optimal set of discriminant vectors and its application to face recognition.Chinese J. Computer, 2000, 23(11): 1189–1195. (in Chinese)Google Scholar
  24. [24]
    Guo Yuefei. Algebraic feature extraction of face images and research on the optimal discriminant vectors [Dissertation]. Nanjing University of Science and Technology, June, 2000. (in Chinese)Google Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2002

Authors and Affiliations

  • Wu Xiaojun 
    • 1
    • 2
    • 4
    Email author
  • Yang Jingyu
    • 2
  • Wang Shitong 
    • 1
    • 2
  • Guo Yuefei
    • 3
  • Cao Qiying 
    • 1
  1. 1.East China Shipbuilding InstituteZhenjiangP.R. China
  2. 2.School of InformationNanjing University of Science & TechnologyNanjingP.R. China
  3. 3.Department of Computer Science and TechnologyFudan UniversityShanghaiP.R. China
  4. 4.Robotics LaboratoryThe Chinese Academy of SciencesShengyangP.R. China

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