Gabor-Eigen-Whiten-Cosine: A Robust Scheme for Face Recognition

  • Weihong Deng
  • Jiani Hu
  • Jun Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3723)


Recognizing faces with complex intrapersonal variations is a challenging task, especially when using small size samples. Our approach, which obtains state of the art results, is based on a new face recognition scheme: Gabor-Eigen-Whiten-Cosine (GEWC). The novelty of this paper lies in 1) the finding that the same face with complex variations, projected into the Gabor based whitened PCA feature space, is approximately angle invariance; and 2) the experimental studies that analyze the joint contribution of Gabor wavelet, whitening process, and cosine similarity measure on the PCA based face recognition. The new GEWC method has been successfully tested and evaluated using comparative experiments on 3000+ FERET frontal face images with 1196 subjects. In particular, the GEWC method achieves constant 100% accuracy on the 200-subject experiment across illuminations and facial expressions. Furthermore, its recognition rates reach up to 96.3%, 99.5%, 78.8%, and 77.8% on the FB, fc, dup I, and dup II probes respectively using only one training sample per person.


Face Recognition Principle Component Analysis Gabor Filter Label Graph Gabor Wavelet 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chellappa, R., Wilson, C., Sirohey, S.: Human and Machine Recognition of Faces: A Survey. Proc. IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  2. 2.
    Adini, Y., Moses, Y., Ullman, S.: Face Recognition:The Problem of Compensating for Changes in Illumination Direction. IEEE Trans. PAMI 19(7), 721–732 (1997)Google Scholar
  3. 3.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  4. 4.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant Analysis of Principal Components for Face Recognition. In: Proc. Third Int’l Conf. Automatic Face and Gesture Recognition, pp. 336–341 (1998)Google Scholar
  5. 5.
    Swets, D.L., Weng, J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. PAMI 16(8), 831–836 (1996)Google Scholar
  6. 6.
    Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  7. 7.
    Phillips, P.J., Wechsler, H., Rauss, P.: The FERET Database and Evaluation Procedure for Face-Recognition Algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  8. 8.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. PAMI 23(2), 228–232 (2001)Google Scholar
  9. 9.
    Wang, X., Tang, X.: A Unified Framework for Subspace Face Recognition. IEEE Trans. PAMI 26(9), 1222–1228 (2004)MathSciNetGoogle Scholar
  10. 10.
    Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R.P., Konen, W.: Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. Computers 42(3), 300–311 (1993)CrossRefGoogle Scholar
  11. 11.
    Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. J. Optical Soc. Am. A 2, 1,160–1,169 (1985)Google Scholar
  12. 12.
    Daugman, J.: Face and Gesture Recognition: Overview. IEEE Trans. PAMI 19(7), 675–676 (1997)Google Scholar
  13. 13.
    Pentland, A., Starner, T., Etcoff, N., Masoiu, N., Oliyide, O., Turk, M.: Experiments with Eigenfaces. In: Proc. Looking at People Workshop Int’l Joint Conf. Artifical Intelligence (1993)Google Scholar
  14. 14.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. PAMI 19(7), 711–720 (1997)Google Scholar
  15. 15.
    Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. PAMI 19(7), 775–779 (1997)Google Scholar
  16. 16.
    Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. PAMI 20(1), 39–51 (1998)Google Scholar
  17. 17.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. PAMI 19(7), 696–710 (1997)Google Scholar
  18. 18.
    Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Trans. PAMI 26(5), 572–581 (2004)Google Scholar
  19. 19.
    Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. PAMI 17(7), 775–779 (1997)Google Scholar
  20. 20.
    Moghaddam, B., Jebara, T., Pentland, A.: Bayesian Face Recognition. Pattern Recognition 33, 1771–1782 (2000)CrossRefGoogle Scholar
  21. 21.
    Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic Classification of Single Facial Images. IEEE Trans. PAMI 21(12), 1357–1362 (1999)Google Scholar
  22. 22.
    Liu, C., Wechsler, H.: Evolutionary Pursuit and Its Application to Face Recognition. IEEE Trans. PAMI 22(6), 570–582 (2000)Google Scholar
  23. 23.
    Gao, W., Cao, B., Shan, S., Zhang, X., Zhou, D.: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations. Technical report of JDL (2004),
  24. 24.
    Deng, W., Hu, J., Guo, J.: Robust Face Recognition from One Training Sample per Person. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 915–924. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Weihong Deng
    • 1
  • Jiani Hu
    • 1
  • Jun Guo
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations