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Face Recognition by Inverse Fisher Discriminant Features

  • Xiao-Sheng Zhuang
  • Dao-Qing Dai
  • P. C. Yuen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

For face recognition task the PCA plus LDA technique is a famous two-phrase framework to deal with high dimensional space and singular cases. In this paper, we examine the theory of this framework: (1) LDA can still fail even after PCA procedure. (2) Some small principal components that might be essential for classification are thrown away after PCA step. (3) The null space of the within-class scatter matrix S w contains discriminative information for classification. To eliminate these deficiencies of the PCA plus LDA method we thus develop a new framework by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results suggest that this new approach works well.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Principle Component Analysis Null Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 12(10), 2385–2404 (2000)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Chen, L.F., Liao, H.Y.M., Lin, J.C., Kao, M.D., Yu, G.J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713–1726 (2000)CrossRefGoogle Scholar
  4. 4.
    Chen, W.S., Yuen, P.C., Huang, J., Dai, D.Q.: Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition. IEEE Trans. on Systems, Man and Cybernetics-part B: Cybernetics 35(4), 657–669 (2005)Google Scholar
  5. 5.
    Dai, D.Q., Yuen, P.C.: Regularized discriminant analysis and its applications to face recognition. Pattern Recognition 36(3), 845–847 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    Dai, D.Q., Yuen, P.C.: A wavelet-based 2-parameter regularization discriminant analysis for face recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 137–144. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Dai, D.Q., Yuen, P.C.: Wavelet based discriminant analysis for face recognition. Applied Math. and Computation (2005) (in press), doi: 10.1016/j.amc.2005.07.044 Google Scholar
  8. 8.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
  9. 9.
    Liu, C.J., Wechsler, H.: A shape- and texture-based enhanced fisher classifier for face recognition. IEEE Trans. Image Processing 10(4), 598–608 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.-R.: Constructing descriptive and discriminative nonlinear features: rayleigh coefficients in kernel feature spaces. IEEE Trans. Pattern Analysis and Machine Intelligence 25(5), 623–628 (2003)CrossRefGoogle Scholar
  11. 11.
    Pima, I., Aladjem, M.: Regularizedd discriminant analysis for face recognition. Pattern Recognition 37, 1945–1948 (2004)CrossRefGoogle Scholar
  12. 12.
    Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13, 252–264 (1991)CrossRefGoogle Scholar
  13. 13.
    Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)CrossRefGoogle Scholar
  14. 14.
    Yang, J., Frangi, A.F., Yang, J.Y., Zhang, D., Jin, Z.: KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 27(2), 230–244 (2005)CrossRefGoogle Scholar
  15. 15.
    Ye, J.P., Janardan, R., Park, C.H., Park, H.: An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(8), 982–994 (2004)CrossRefGoogle Scholar
  16. 16.
    Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)MATHCrossRefGoogle Scholar
  17. 17.
    Zhang, B., Zhang, H., Sam Ge, S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Transactions on Neural Networks 15(1), 166–177 (2004)CrossRefGoogle Scholar
  18. 18.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–459 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiao-Sheng Zhuang
    • 1
  • Dao-Qing Dai
    • 1
  • P. C. Yuen
    • 2
  1. 1.Center for Computer Vision and Department of MathematicsSun Yat-Sen(Zhongshan)UniversityGuangzhouChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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