Face Recognition Using Nonlinear Partial Least Squares in Reproducing Kernel Hilbert Space

  • Ye-Gang Hu
  • Chuan-Xian Ren
  • Yun-Fei Yao
  • Wan-Yi Li
  • Feng-Wang
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


Dimensionality reduction is an important step for face recognition. A novel feature extraction method using nonlinear partial least squares discrimination (NPLSD) for facial images is proposed in this paper. The method is constituted by two consecutive steps, in which the original facial features are projected onto the reproducing kernel Hilbert space to solve the linear inseparable problem, then the iterative and supervised partial least squares method is used for calculating subsequent discriminant components, wherein an approach for modeling a relationship between a set of input variables and response variables, while maintaining most of the variance in the input variables, and then extract the discrimination features from different classes, until convergence of latent vectors by more iterations. This will result in a dramatic reduction for the computational time since the calculation for the covariance matrix is not required. The experiment results using the ORL data demonstrates the effectiveness of the proposed method.


Face recognition nonlinear mapping kernel learning partial least squares iterative process 


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  1. 1.
    Jain, A.K., Duin, R.P.W., Mao, J.C.: Statistical Pattern Recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)CrossRefGoogle Scholar
  4. 4.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  5. 5.
    Wold, H.: Soft Modeling by Latent Variables; the Nonlinear Iterative Partial Least Squares Approach. In: Gani, J. (ed.) Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett, pp. 520–540. Academic Press, London (1975)Google Scholar
  6. 6.
    Wold, S., Ruhe, H., Wold, H., Dunn III, W.J.: The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverse. SIAM Journal of Scientific and Statistical Computations 5, 735–743 (1984)zbMATHGoogle Scholar
  7. 7.
    Worsley, K.: An Overview and Some New Developments in the Statistical Analysis of PET and fMRI Data. Human Brain Mapping 5, 254–258 (1997)CrossRefGoogle Scholar
  8. 8.
    Baek, J., Kim, M.: Face Recognition using Partial Least Squares Components. Pattern Recognition 37(6), 1303–1306 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Sharma, A., Jacobs, D.W.: Bypassing Synthesis: PLS for Face Recognition with Pose. Low-Resolution and Sketch. In: IEEE CVPR, pp. 593–600 (2011)Google Scholar
  10. 10.
    Boulesteix, A.L., Strimmer, K.: Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data. Briefings in Bioinformatics 8(1), 32–44 (2007)CrossRefGoogle Scholar
  11. 11.
    Frank, I.E.: A Nonlinear PLS model. Chemolab 8, 109–119 (1990)Google Scholar
  12. 12.
    Malthouse, E.C., Tamhane, A.C., Mah, R.S.H.: Nonlinear Partial Least Squares. Computers in Chemical Engineering 21(8), 875–890 (1997)CrossRefGoogle Scholar
  13. 13.
    Rosipal, R.: Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research 2, 97–123 (2001)Google Scholar
  14. 14.
    Rosipal, R.: Nonlinear Partial Least Squares: An Overview. In: Lodhi, H., Yamanishi, Y. (eds.) Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, pp. 169–189 (2011)Google Scholar
  15. 15.
    Lewi, P.J.: Pattern Recognition, Reflection from a Chemometric Point of View. Chemometrics and Intelligent Laboratory Systems 28, 23–33 (1995)Google Scholar
  16. 16.
    Liu, B.Y.: Adaptive Training of a Kernel-based Nonlinear Discriminator. Pattern Recognition 38, 2419–2425 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ye-Gang Hu
    • 1
  • Chuan-Xian Ren
    • 2
  • Yun-Fei Yao
    • 1
  • Wan-Yi Li
    • 3
  • Feng-Wang
    • 4
  1. 1.Department of MathematicsFuyang Normal CollegeFuyangChina
  2. 2.Center for Computer Vision, Department of MathematicsSun Yat-Sen UniversityGuangzhouChina
  3. 3.Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Department of Computer and InformationFuyang Normal CollegeFuyangChina

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