Skip to main content
SpringerLink
Log in

Complete discriminant evaluation and feature extraction in kernel space for face recognition

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Georgia tech face database. http://www.anefian.com/face_reco.htm

  2. Beveridge, R., Bolme, D., Teixeira, M., Draper, B.: The csu face identification evaluation system users guide: Version 5.0. Technical Report: http://www.cs.colostate.edu/evalfacerec/data/normalization.html (2003)

  3. Cevikalp H., Neamtu M., Wilkes M., Barkana A. (2005). Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(1): 4–13

    Article  Google Scholar 

  4. Chen L.F., Liao H.Y.M., Ko M.T., Lin J.C., Yu G.J. (2000). A new lda-based face recognition system which can solve the small sample size problem. Pattern Recogn. 33(10): 1713–1726

    Article  Google Scholar 

  5. Chen W.S., Yuen P.C., Huang J., Dai D.Q. (2005). Kernel machine-based one-parameter regularized fisher discriminant method for face recognition. IEEE Trans. Syst. Man Cybern. Part B 35(4): 659–669

    Article  Google Scholar 

  6. Friedman J.H. (1989). Regularized discriminant analysis. J. Am. Stat. Assoc. 84(405): 165–175

    Article  Google Scholar 

  7. Fukunaga K. (1990). Introduction to Statistical Pattern Recognition. Academic, San Diego

    MATH  Google Scholar 

  8. Gupta, H., Agarwal, A.K., Pruti, T., Shekhar, C., Chellappa, R.: An experiment evaluation of linear and kernel-based methods for face recognition. IEEE Workshop Appl. Comput. Vis. (2002)

  9. Jiang X.D., Mandal B., Kot A. (2006). Enhanced maximum likelihood face recognition. IEE Elect. Lett. 42(19): 1089–1090

    Article  Google Scholar 

  10. Jiang, X.D., Mandal, B., Kot, A.: Eigenfeature regularization and extraction in face recognition. IEEE Trans. Pattern Anal. Machine Intell. 30, DOI: 10.1109/TPAMI.2007.70,708 (2008)

  11. Kin K.I., Jung K., Kim H.J. (2002). Face recognition using kernel principal component analysis. IEEE Signal Process. Lett. 9(2): 40–42

    Article  Google Scholar 

  12. Liu K., Cheng Y.Q., Yang J.Y., Liu X. (1992). An efficient algorithm for foley-sammon optical set of discriminant vectors by algebraic method. Int. J. Pattern Recogn. Artif. Intell. 6: 817–829

    Article  Google Scholar 

  13. Liu, Q.S., Huang, R., Lu, H.Q., Ma, S.D.: Face recognition using kernel based fisher discriminant analysis. In: Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 197–201 (2002)

  14. Liu Q.S., Lu H., Ma S. (2004). Improving kernel fisher discriminant analysis for face recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1): 42–49

    Article  Google Scholar 

  15. Liu Q.S., Lu H.Q., Ma S.D. (2004). Improving kernel fisher discriminant analysis for face recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1): 42–49

    Article  Google Scholar 

  16. Liu Q.S., Tang X., Lu H., Ma S.D. (2006). Face recognition using kernel scatter-difference-based discriminant analysis. IEEE Trans. Neural Netw. 17(4): 1081–1085

    Article  Google Scholar 

  17. Liu, W., Wang, Y.W., Li, S.Z., Tan, T.N.: Null space-based kernel fisher discriminant analysis for face recognition. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 369–374 (2004)

  18. Lu J., Plataniotis K.N., Venetsanopoulos A.N. (2003). Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Netw. 14(1): 117–126

    Article  Google Scholar 

  19. Lu J., Plataniotis K.N., Venetsanopoulos A.N. (2003). Face recognition using lda-based algorithms. IEEE Trans. Neural Netw. 14(1): 195–200

    Article  Google Scholar 

  20. Lu J., Plataniotis K.N., Venetsanopoulos A.N., Li S.Z. (2006). Ensemble-based discriminant learning with boosting for face recognition. IEEE Trans. Neural Netw. 17(1): 166–178

    Article  Google Scholar 

  21. Yang, M., Ahuja, N., Kriegman, D.: Face recognition using kernel eigenfaces. IEEE Conf. Image Process. pp. 37–40 (2000)

  22. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.R.: Fisher discriminant analysis with kernels. IEEE Workshop Neural Netw. Signal Process. IX, pp. 41–48 (1999)

  23. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Smola, A.J., Muller, K.R., Solla, S.A., Leen, T.K.: Invariant feature extraction and classification in kernel spaces. Adv. Neural Inform. Process. Syst. MIT Press, Cambridge, vol. 12, pp. 526–532 (2000)

  24. Moghaddam B., Pentland A. (1997). Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19(7): 696–710

    Article  Google Scholar 

  25. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. IEEE Conf. Comp. Vis. Pattern Recogn. pp. 947–954 (2005)

  26. Phillips P.J., Moon H., Rizvi S., Rauss P. (2000). The feret evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10): 1090–1104

    Article  Google Scholar 

  27. Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142. Sarasota, FL, USA (1994)

  28. Scholkopf B., Mika S., Burges C.J., Knirsch P., Muller K.R., Ratsch G., Smola A. (1999). Input space versus feature space in kernel-based methods. IEEE Trans. Neural Netw. 10: 1000–1017

    Article  Google Scholar 

  29. Scholkopf B., Smola A., Muller K.R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5): 1299–1319

    Article  Google Scholar 

  30. Shakhnarovich G., Moghaddam B. (2004). Face Recognition in Subspaces. Springer, Heidelberg

    Google Scholar 

  31. Vapnik V.N. (1998). Statistical Learning Theory. Wiley, New York

    MATH  Google Scholar 

  32. Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. IEEE Conf. Comput. Vis. Pattern Recogn. 2, pp. 564–569 (2004)

  33. Yang J., Frangi A.F., Yang J.Y., Zhang D., Jin Z. (2005). Kpca plus lda: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2): 230–244

    Article  Google Scholar 

  34. Yang, M.H.: Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: Proceedings 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 215–220 (2002)

  35. Zhao W., Chellappa R., Phillips P.J., Rosenfeld A. (2003). Face recognition: a literature survey. ACM Comput. Surv. 35(4): 399–458

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Xudong Jiang, Bappaditya Mandal & Alex Kot

Authors
  1. Xudong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bappaditya Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex Kot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xudong Jiang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, X., Mandal, B. & Kot, A. Complete discriminant evaluation and feature extraction in kernel space for face recognition. Machine Vision and Applications 20, 35–46 (2009). https://doi.org/10.1007/s00138-007-0103-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-007-0103-1

Keywords

Search

Navigation