Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Illumination Compensation

  • Xudong Xie
  • Kin-Man Lam
  • Qionghai Dai
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_300



Due to difficulty in controlling the lighting conditions in practical applications, variable illumination is one of the most challenging tasks in face recognition. Prior to face recognition, illumination compensation has to be performed, whereby the uneven illumination of human faces is compensated and face images in normal lighting conditions are reconstructed. The reconstructed face images are then used for classification. An illumination compensation scheme includes the following modules: lighting category evaluation, shape normalization, and lighting compensation.


Human face recognition, one of the most successful applications of image analysis and understanding, has received significant attention in the last decade. However, due to difficulty in controlling the lighting conditions in practical applications, variable illumination is one of the most daunting challenges in face recognition. As stated...
This is a preview of subscription content, log in to check access.


  1. 1.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE T. Pattern Anal. 19(7), 721–732 (1997)CrossRefGoogle Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE T. Neural Networ. 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  4. 4.
    Yale University [Online]. Available at: http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
  5. 5.
    Pizer, S.M., Amburn, E.P.: Adaptive histogram equalization and its variations. Comput. Vision Graph. 39, 355–368 (1987)CrossRefGoogle Scholar
  6. 6.
    Zhu, J., Liu, B., Schwartz, S.C.: General illumination correction and its application to face normalization. In proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 133–136. Hong Kong, China, (2003)Google Scholar
  7. 7.
    Xie, X., Lam, K.M.: Face recognition under varying illumination based on a 2D face shape model. Pattern Recogn. 38(2), 221–230 (2005)CrossRefGoogle Scholar
  8. 8.
    Xie, X., Lam, K.M.: An efficient illumination normalization method for face recognition. Pattern Recogn. Lett. 27(6), 609–617 (2006)CrossRefGoogle Scholar
  9. 9.
    Zhao, J., Su, Y., Wang, D., Luo, S.: Illumination ratio image: synthesizing and recognition with varying illuminations. Pattern Recogn. Lett. 24(15), 2703–2710 (2003)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Wechsler, H.: A shape- and texture-based enhanced fisher classifier for face recognition. IEEE T. Image Process. 10(4), 598–608 (2001)MATHCrossRefGoogle Scholar
  11. 11.
    Goshtasby, A.: Piecewise cubic mapping functions for image registration. Pattern Recogn. 20(5), 525–533 (1987)CrossRefGoogle Scholar
  12. 12.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE T. Pattern Anal. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  13. 13.
    Yale University [Online]. Available at: http://cvc.yale.edu/projects/yalefaces/yalefaces.html
  14. 14.
    Martinez, A.M., Benavente, R.: The AR face database, CVC technical report #24 (1998)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Xudong Xie
    • 1
  • Kin-Man Lam
    • 2
  • Qionghai Dai
    • 1
  1. 1.Automation DepartmentTsinghua UniversityBeijingChina
  2. 2.Department of Electronic and Information EngineeringThe Hong Kong Polytechnic UniversityHong KongChina