Deterioration of visual information in face classification using Eigenfaces and Fisherfaces

  • Gabriel Jarillo Alvarado
  • Witold Pedrycz
  • M. Reformat
  • Keun-Chang Kwak
Original Paper


In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-à-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers.


Face recognition Deterioration of visual information Principal component analysis Linear discriminant analysis Fisherfaces and Eigenfaces FERET face database 


  1. 1.
    Joo Er, M., Wu, S., Lu, J., Lye Toh, H.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Netw. 13(3), 697–710 (2002)CrossRefGoogle Scholar
  2. 2.
    Bolle, R.M. et al.: Guide to Biometrics. Springer-Verlag, Berlin Heidelberg New York (2004)Google Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Netw. 14(1), 117–126 (2003)CrossRefGoogle Scholar
  5. 5.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using LDA-based algorithms. IEEE Trans. Neural Netw. 14(1), 195–126–208 (2003)CrossRefGoogle Scholar
  6. 6.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Recognit. Anal. Machine Intell. 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  7. 7.
    Turk, M., Pentland, A.: Face recognition using Eigenfaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  8. 8.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Amer. A 4(3), 519–524 (1987)CrossRefGoogle Scholar
  9. 9.
    Martínez, A.M., Avinash, C.K.: PCA versus LDA. IEEE Trans. Pattern Analy. Machine Intell. 23(2), 228–233 (2001)CrossRefGoogle Scholar
  10. 10.
    Etemad, K., Chellappa, K.: Discriminant analysis for recognition of human face images. J. Opt. Soc. Amer. A 14(8), 1724–1733 (1997)CrossRefGoogle Scholar
  11. 11.
    Cios, K., Pedrycz, W., Swiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers, second printing (2000)Google Scholar
  12. 12.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press (1996)Google Scholar
  13. 13.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory IT-13, 21–27 (1967)CrossRefGoogle Scholar
  14. 14.
    Phillips, P.J. et al.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. J. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  15. 15.
    Pentland, A., Choudhury, T.: Face recognition for smart environments. IEEE Computer J. 33(2), 50–55 (2000)Google Scholar
  16. 16.
    Frey, B.J., Colmenarez, A., Huang, T.S.: Mixtures of local linear subspaces for face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 32–37 (1998)Google Scholar
  17. 17.
    Liu, C., Wechsler, H.: A unified Bayesian framework for face recognition. In: Proceedings IEEE of the International Conference on Image Processing, vol. 1, pp. 151–155 (1998)Google Scholar
  18. 18.
    Perlibakas, V.: Distance measure for PCA-based face recognition. Pattern Recognit. Let. 25(6), 711–724 (2004)CrossRefGoogle Scholar
  19. 19.
    Zhao, W., Chellappa, R.: Robust image based face recognition. In: Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 41–44 (2000)Google Scholar
  20. 20.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings Of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 336–341 (1998)Google Scholar
  21. 21.
    Ekstrom, M.P.: Digital Image Processing Techniques. Academic Press (1984)Google Scholar
  22. 22.
    Samaria, F.S., Harter, C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)Google Scholar
  23. 23.
    Stainvas, I., Intrator, N.: Blurred face recognition via a hybrid neural architecture. In: Proceedings IEEE of the 15th International Conference on Pattern Recognition, vol. 2, pp. 805–808 (2000)Google Scholar
  24. 24.
    McGuire, P., D'Eleuterio, G.M.T.: Eigenpaxels and a neural network approach to image classification. IEEE Trans. Neural Netw. 12(3), 625–635 (2001)CrossRefGoogle Scholar
  25. 25.
    Stewart Bartlett, M., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Gabriel Jarillo Alvarado
    • 1
  • Witold Pedrycz
    • 1
  • M. Reformat
    • 1
  • Keun-Chang Kwak
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada

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