On Improving the Efficiency of Eigenface Using a Novel Facial Feature Localization

  • Aleksey Izmailov
  • Adam Krzyżak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Face recognition is the most popular non-intrusive biometric technique with numerous applications in commerce, security and surveillance. Despite its good potential, most of the face recognition methods in the literature are not practical due to the lack of robustness, slow recognition, and semi-manual localizations. In this paper, we improve the robustness of eigenface-based systems with respect to variations in illumination level, pose and background. We propose a new method for face cropping and alignment which is fully automated and we integrate this method in Eigenface algorithm for face recognition. We also investigate the effect of various preprocessing techniques and several distance metrics on the overall system performance. The evaluation of this method under single-sample and multi-sample recognition is presented. The results of our comprehensive experiments on two databases, FERET and JRFD, show a significant gain compared to basic Eigenface method and considerable improvement with respect to recognition accuracy when compared with previously reported results in the literature.


Face Recognition Face Image Mahalanobis Distance Face Detection Histogram Equalization 
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.


  1. 1.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2d and 3d face recognition: A survey. Pattern Recog. Lett. 28(14), 1885–1906 (2007)CrossRefGoogle Scholar
  2. 2.
    Chen, X., Flynn, P.J., Bowyer, K.W.: Fully automated facial symmetry axis detection in frontal color images. In: AUTOID 2005: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies, Washington, DC, USA, pp. 106–111. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  5. 5.
    Hongtao, S., Feng, D.D., Rong-chun, Z.: Face recognition using multi-feature and radial basis function network. In: VIP 2002: Selected papers from the 2002 Pan-Sydney workshop on Visualisation, pp. 51–57. Australian Computer Society, Inc., Darlinghurst (2002)Google Scholar
  6. 6.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)CrossRefGoogle Scholar
  7. 7.
    Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition: a review. Comput. Vis. Image Underst. 97(1), 103–135 (2005)CrossRefGoogle Scholar
  8. 8.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Nakao, N., Ohyama, W., Wakabayashi, T., Kimura, F.: Automatic detection of facial midline as a guide for facial feature extraction. In: PRIS, pp. 119–128 (2007)Google Scholar
  10. 10.
    Perlibakas, V.: Distance measures for pca-based face recognition. Pattern Recog. Lett. 25(6), 711–724 (2004)CrossRefGoogle Scholar
  11. 11.
    Rokita, J.: Multimodal biometric system based on face and hand images taken by a cell phone. Master’s thesis, Computer Science Dept., Concordia University, Montreal, Quebec, Canada (March 2008)Google Scholar
  12. 12.
    Rokita, J., Krzyżak, A., Suen, C.Y.: Cell phones personal authentication systems using multimodal biometrics. In: International Conference on Image Analysis and Recognition, pp. 1013–1022 (2008)Google Scholar
  13. 13.
    Rokita, J., Krzyżak, A., Suen, C.Y.: Multimodal biometrics by face and hand images taken by a cell phone camera. International Journal of Pattern Recognition and Artificial Intelligence, 411–429 (2008)Google Scholar
  14. 14.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America 4(3), 519–524 (1987)CrossRefGoogle Scholar
  15. 15.
    Tan, X., Chen, S., Zhou, Z.-H., Zhang, F.: Face recognition from a single image per person: A survey. Pattern Recog. 39(9), 1725–1745 (2006)CrossRefzbMATHGoogle Scholar
  16. 16.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  17. 17.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (2001)Google Scholar
  18. 18.
    Woodward, J.D., Orlans, N.M.: Biometrics. McGraw-Hill, Inc., New York (2002)Google Scholar
  19. 19.
    Yan, Y., Zhang, Y.-J.: Tensor correlation filter based class-dependence feature analysis for face recognition. Neurocomputing 71(16-18), 3434–3438 (2008)CrossRefGoogle Scholar
  20. 20.
    Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aleksey Izmailov
    • 1
  • Adam Krzyżak
    • 1
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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