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An Image Preprocessing Algorithm for Illumination Invariant Face Recognition

  • Ralph Gross
  • Vladimir Brajovic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. State-of-the-art commercial face recognition algorithms still struggle with this problem. We propose a new image preprocessing algorithm that compensates for illumination variations in images. From a single brightness image the algorithm first estimates the illumination field and then compensates for it to mostly recover the scene reflectance. Unlike previously proposed approaches for illumination compensation, our algorithm does not require any training steps, knowledge of 3D face models or reflective surface models. We apply the algorithm to face images prior to recognition. We demonstrate large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases.

Keywords

Face Recognition Recognition Accuracy Illumination Condition Face Database Illumination Variation 
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.

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References

  1. [1]
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE PAMI 22 (2000) 1090–1104Google Scholar
  2. [2]
    Blackburn, D., Bone, M., Philips, P.: Facial recognition vendor test 2000: evaluation report (2000)Google Scholar
  3. [3]
    Gross, R., Shi, J., Cohn, J.: Quo vadis face recognition? In: Third Workshop on Empirical Evaluation Methods in Computer Vision. (2001)Google Scholar
  4. [4]
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE PAMI 19 (1997) 711–720Google Scholar
  5. [5]
    Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible lighting conditions. Int. J. of Computer Vision 28 (1998) 245–260CrossRefGoogle Scholar
  6. [6]
    Georghiades, A., Kriegman, D., Belhumeur, P.: From few to many: Generative models for recognition under variable pose and illumination. IEEE PAMI (2001)Google Scholar
  7. [7]
    Riklin-Raviv, T., Shashua, A.: The Quotient image: class-based re-rendering and recognition with varying illumination conditions. In: IEEE PAMI. (2001)Google Scholar
  8. [8]
    Georghiades, A., Kriegman, D., Belhumeur, P.: Illumination cones for recognition under variable lighting: Faces. In: Proc. IEEE Conf. on CVPR. (1998)Google Scholar
  9. [9]
    Blanz, V., Romdhani, S., Vetter, T.: Face identification across different poses and illumination with a 3D morphable model. In: IEEE Conf. on Automatic Face and Gesture Recognition. (2002)Google Scholar
  10. [10]
    Horn, B.: Robot Vision. MIT Press (1986)Google Scholar
  11. [11]
    Stockam, T.: Image processing in the context of a visual model. Proceedings of the IEEE 60 (1972) 828–842Google Scholar
  12. [12]
    Land, E., McCann, J.: Lightness and retinex theory. Journal of the Optical Society of America 61 (1971)Google Scholar
  13. [13]
    Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color imges and the human observation of scenes. IEEE Trans. on Image Processing 6 (1997)Google Scholar
  14. [14]
    Tumblin, J., Turk, G.: LCIS: A boundary hierarchy for detail-preserving contrast reduction. In: ACM SIGGRAPH. (1999)Google Scholar
  15. [15]
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE PAMI 12 (1990) 629–639Google Scholar
  16. [16]
    Wandel, B.: Foundations of Vision. Sunderland MA: Sinauer (1995)Google Scholar
  17. [17]
    Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes in C. Cambridge University Press (1992)Google Scholar
  18. [18]
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression (PIE) database. In: IEEE Int. Conf. on Automatic Face and Gesture Recognition. (2002)Google Scholar
  19. [19]
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 (1991) 71–86CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ralph Gross
    • 1
  • Vladimir Brajovic
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburgh

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