Gabor Features Based Method Using HDR (G-HDR) for Multiview Face Recognition

  • Dan Yao
  • Xiangyang Xue
  • Yufei Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3338)


This paper introduces a novel algorithm named G-HDR, which is a Gabor features based method using Hierarchical Discriminant Regression (HDR) for multiview face recognition. Gabor features help to eliminate the influences to faces such as changes in illumination directions and expressions; Modified HDR tree help to get a more precise classify tree to realize the coarse-to-fine retrieval process. The most challenging things in face recognition are the illumination variation problem and the pose variation problem. The goal of Our G-HDR is to overcome both difficulties. We conducted experiments on the UMIST database and Volker Blanz’s database and got good results.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hwang, W.-S., Weng, J.: Hierarchical Discriminant Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11) (2000)Google Scholar
  2. 2.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  3. 3.
    Fazel-Rezai, R., Kinsner, W.: Image analysis and reconstruction using complex Gabor wavelets. In: Canadian Conference on Electrical and Computer Engineering2000, vol. 1, pp. 440–444 (2000)Google Scholar
  4. 4.
    Tao, L., Kwan, H.K.: Real-valued discrete Gabor transform for image representation. In: IEEE International Symposium on Circuits and Systems. ISCAS 2001., vol. 2, pp. 589–592 (2001)Google Scholar
  5. 5.
    Daugman, J.G.: Uncertainly relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J.Opt.Soc.Amer.A. 2, 1160–1169 (1985)CrossRefGoogle Scholar
  6. 6.
    Blanz, V., Vetter, T.: Face Recognition Based on 3D Shape Estimation from Single Images. Computer Graphics Technical Report No.2, University of Freiburg (2002)Google Scholar
  7. 7.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys (CSUR) 35(4), 399–458 (2003)CrossRefGoogle Scholar
  8. 8.
    Graham, D.B., Allinson, N.M.: In: Wechsler, H., Phillips, P.J., Bruce, V., Fogelman-Soulie, F., Huang, T.S. (eds.) Face Recognition: From Theory to Applications. NATO ASI Series F. Computer and Systems Sciences, vol. 163, pp. 446–456 (1998)Google Scholar
  9. 9.
    Schiele, B., Crowley, J.L.: Recognition without correspondence using multidimensional receptive field histograms. Int. J. Comput.Vis. 36(1), 31–52 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dan Yao
    • 1
  • Xiangyang Xue
    • 1
  • Yufei Guo
    • 1
  1. 1.Dept. of Computer Science and EngineeringFuDan UniversityShanghaiChina

Personalised recommendations