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Improvements on CCA Model with Application to Face Recognition

  • Quan-Sen Sun
  • Mao-Long Yang
  • Pheng-Ann Heng
  • De-Sen Xia
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 163)

Abstract

Two new methods for combination feature extraction are proposed in this paper. The methods are based on the framework of CCA in image recognition by improving the correlation criterion functions. Comparing with CCA methods, which can solve the classification of high-dimensional small size samples directly, being independent of the total scatter matrix singularity of the training simples, and the algorithms’ complexity can be lowered. We prove that the essence of two improved criterion functions is partial least squares analysis (PLS) and multivariate linear regression (MLR). Experimental results based on ORL standard face database show that the algorithms are efficient and robust.

Key words

canonical correlation analysis(CCA) feature extraction feature fusion partial least squares (PLS) multivariate linear regression(MLR) face recognition 

References

  1. 1.
    Yang Jian, Yang Jing-yu, Zhang David, Lu Jian-feng, Feature fusion: parallel strategy vs. serial strategy, Pattern Recognition, 36 (2003) 1961–1971.CrossRefGoogle Scholar
  2. 2.
    Liu C J, Wechsler H, A shape-and texture-based enhanced Fisher classifier for face recognition, IEEE Transactions on Image Processing, 10(4) (2001)598–608.CrossRefzbMATHGoogle Scholar
  3. 3.
    Quan-Sen Sun, Sheng-Gen Zeng, Yan Liu, Pheng-Ann Heng, De-Shen Xia. A new method of feature fusion and its application in image recognition. Pattern Recognition(USA), in review.Google Scholar
  4. 5.
    A. Höskuldsson, PLS regression methods, Journal of Chemometrics, 2(1988)211–228.CrossRefGoogle Scholar
  5. 6.
    M. Borga, Learning Multidimensional Signal Processing, Linköping Studies in Science and Technology, issertations, No.531, Department of Electrical Engineering, Linköping University, Linköping, Sweden, 1998.Google Scholar
  6. 7.
    Turk M and Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience, 1991, 3(1):71–86.CrossRefGoogle Scholar
  7. 8.
    Peter N. Belhumeur, et al. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19(7) (1997) 711–720.CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2005

Authors and Affiliations

  • Quan-Sen Sun
    • 1
    • 2
  • Mao-Long Yang
    • 1
  • Pheng-Ann Heng
    • 3
  • De-Sen Xia
    • 1
  1. 1.Department of Computer ScienceNanjing University of Science &TechnologyNanjingPeople’ Republic of China
  2. 2.Department of MathematicsJinan UniversityJinanPeople’ Republic of China
  3. 3.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong Kong

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