Retinex Combined with Total Variation for Image Illumination Normalization

  • Luigi Cinque
  • Gabriele Morrone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


This paper presents a method for the normalization of human facial images in arbitrary illumination conditions. The enhanced image is suitable to be used as an input to a face recognition system.


Face Recognition Facial Image Face Recognition System Photometric Stereo Histogram Match 
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.
    Land, E.H., McCann, J.J.: Lightness and Retinex Theory. J. Opt. Soc. Am. 61(1) (1971)Google Scholar
  2. 2.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observation of Scenes. IEEE Transactions on Image Processing 6(3), 965–976 (1997)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Kriegman, D.J.: What is the set of images of an object under all possible lighting conditions? IEEE International Conference on Computer Vision and Pattern Recognition (1996)Google Scholar
  4. 4.
    Bhattacharyya, J.: Detecting Removing Specularities and Shadows in Images. Masters Thesis, Department of Electrical and Computer Engineering, McGill University (June 2004)Google Scholar
  5. 5.
    Rammamorthi, R., Hanrahan, P.: A Signal-Processing Framework for Inverse Rendering. In: ACM SIGGRAPH (2001)Google Scholar
  6. 6.
    Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Illumination normalization for face recognition and uneven background correction using total variation based image models. In: Proceedings of the IEEE Computer Society CVPR, pp. 532–539 (2005)Google Scholar
  7. 7.
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20, 89–97 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lee, K.C., Ho, J., Kriegman, D.: Acquiring Linear Subspaces for Face Recognition under Variable Lighting. IEEE Trans. Pattern Anal. Mach. Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines(2001),
  11. 11.
    Hayakawa, H.: Photometric stereo under light source with arbitrary motion. Journal of Optical Society of America A, 11 (1994)Google Scholar
  12. 12.
    Horn, B., Brooks, M. (eds.): Shape from Shading. MIT Press, Cambridge (1989)Google Scholar
  13. 13.
    Shashua, A.: On photometric issues in 3d visual recognition from a single 2D image. International Journal of Computer Vision 21, 99–122 (1997)CrossRefGoogle Scholar
  14. 14.
    Yuille, A.L., Snow, D., Epstein, R., Belhumeur, P.N.: Determining generative models of objects under varying illumination: Shape and albedo from multiple images using svd and integrability. International Journal of Computer Vision 35, 203–222 (1999)CrossRefGoogle Scholar
  15. 15.
    Wang, H., Li, S.Z., Wang, Y.: Generalized quotient image. IEEE International Conference on Computer Vision and Pattern Recognition (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luigi Cinque
    • 1
  • Gabriele Morrone
    • 1
  1. 1.Dipartimento di Informatica“Sapienza” Università di RomaRomaItaly

Personalised recommendations