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Lighting Aware Preprocessing for Face Recognition across Varying Illumination

  • Hu Han
  • Shiguang Shan
  • Laiyun Qing
  • Xilin Chen
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

Illumination variation is one of intractable yet crucial problems in face recognition and many lighting normalization approaches have been proposed in the past decades. Nevertheless, most of them preprocess all the face images in the same way thus without considering the specific lighting in each face image. In this paper, we propose a lighting aware preprocessing (LAP) method, which performs adaptive preprocessing for each testing image according to its lighting attribute. Specifically, the lighting attribute of a testing face image is first estimated by using spherical harmonic model. Then, a von Mises-Fisher (vMF) distribution learnt from a training set is exploited to model the probability that the estimated lighting belongs to normal lighting. Based on this probability, adaptive preprocessing is performed to normalize the lighting variation in the input image. Extensive experiments on Extended YaleB and Multi-PIE face databases show the effectiveness of our proposed method.

Keywords

Face Recognition Face Image Face Database Normal Lighting Illumination Normalization 
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.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Gonzalez, R., Woods, R.: Digital image processing, pp. 91–94. Prentice Hall, USA (1992)Google Scholar
  3. 3.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. PAMI 19, 721–732 (1997)Google Scholar
  4. 4.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. IP 6, 451–462 (1997)Google Scholar
  5. 5.
    Land, E.H.: An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. Nati. Acad. Sci. USA 83, 3078–3080 (1986)CrossRefGoogle Scholar
  6. 6.
    Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: Proc. AMFG, Nice, pp. 157–164 (2003)Google Scholar
  7. 7.
    Shashua, A., Raviv, T.R.: The quotient image: Class-based re-rendering and recog- nition with varying illuminations. IEEE Trans. PAMI 23, 129–139 (2001)Google Scholar
  8. 8.
    Wang, H., Li, S., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: Proc. FG, Seoul, pp. 819–824 (2004)Google Scholar
  9. 9.
    Nishiyama, M., Yamaguchi, O.: Face recognition using the classified appearancee-based quotient image. In: Proc. FG, Southampton, pp. 49–54 (2006)Google Scholar
  10. 10.
    Xie, X., Lam, K.: An efficient illumination normalization method for face recognition. Pattern Recognition Letters 27, 609–617 (2006)CrossRefGoogle Scholar
  11. 11.
    Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. SMC:B 36, 458–466 (2006)Google Scholar
  12. 12.
    Chan, T., Esedoglu, S.: Aspects of total variation regularized l1 function approximation. CAM Report, 4–7 (2004)Google Scholar
  13. 13.
    Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. PAMI 28, 1519–1524 (2006)Google Scholar
  14. 14.
    Xie, X., Zheng, W., Lai, J., Yuen, P.C.: Face illumination normalization on large and small scale features. In: Proc. CVPR, Alaska, pp. 1–8 (2008)Google Scholar
  15. 15.
    Di, W., Zhang, L., Zhang, D., Pan, Q.: Studies on hyperspectral face recognition with feature band selection. IEEE Trans. SMC-A (to appear)Google Scholar
  16. 16.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Proc. ICCV Workshop, Rio de Janeiro, pp. 168–182 (2007)Google Scholar
  17. 17.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI 23, 643–660 (2001)Google Scholar
  18. 18.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. PAMI 19, 711–720 (1997)Google Scholar
  19. 19.
    Du, B., Shan, S., Qing, L., Gao, W.: Empirical comparisons of several preprocessing methods for illumination insensitive face recognition. In: Proc. ICASSP, Pennsylvania, pp. 981–984 (2005)Google Scholar
  20. 20.
    Horn, B.K.P., Brooks, M.J.: The variational approach to shape from shading. CVGIP 33, 174–208 (1986)Google Scholar
  21. 21.
    Shashua, A.: On photometric issues in 3d visual recognition from a single 2d image. IJCV 21, 99–122 (1997)CrossRefGoogle Scholar
  22. 22.
    Hallinan, P.W.: A low-dimensional representation of human faces for arbitrary lighting conditions. In: Proc. CVPR, Seattle, pp. 995–999 (1994)Google Scholar
  23. 23.
    Basri, R., Jacobs, D.W.: Lambertian reectance and linear subspaces. IEEE Trans. PAMI 25, 218–233 (2003)Google Scholar
  24. 24.
    Belhumeur, P.N., Kriegman, D.J.: What is the set of images of an object under all possible illumination conditions? IJCV 28, 245–260 (1998)CrossRefGoogle Scholar
  25. 25.
    Ramamoorthi, R., Hanrahan, P.: On the relationship between radiance and irradiance: determining the illumination from images of a convex lambertian object. JOSA 18, 2448–2459 (2001)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Zhang, L., Samaras, D.: Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans. PAMI 28, 351–363 (2006)Google Scholar
  27. 27.
    Qing, L., Shan, S., Gao, W., Du, B.: Face recognition under generic illumination based on harmonic relighting. IJPRAI 19, 513–531 (2005)Google Scholar
  28. 28.
    Wang, Y., Liu, Z., Hua, G., Wen, Z., Zhang, Z., Samaras, D.: Face re-lighting from a single image under harsh lighting conditions. In: Proc. CVPR, Minnesota, pp. 1–8 (2007)Google Scholar
  29. 29.
    Jiang, X., Kong, Y.O., Huang, J., Zhao, R.-c., Zhang, Y.: Learning from real images to model lighting variations for face images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 284–297. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Forsyth, D.A., Ponce, J.: Computer vision: A modern approach, pp. 46–58. Prentice Hall, USA (2002)Google Scholar
  31. 31.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image and Vision Computing 28, 807–813 (2010)CrossRefGoogle Scholar
  32. 32.
    Mardia, K.V., Jupp, P.E.: Directional statistics, pp. 36–44. J. Wiley, Chichester (2000)zbMATHGoogle Scholar
  33. 33.
    Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von mises-fisher distributions. JMLR 9, 1345–1382 (2005)MathSciNetGoogle Scholar
  34. 34.
    Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression database. IEEE Trans. PAMI 25, 1615–1618 (2003)Google Scholar
  35. 35.
    Choi, S., Kim, C., Choi, C.: Shadow compensation in 2d images for face recognition. Pattern Recognition 40, 2118–2125 (2007)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hu Han
    • 1
    • 2
  • Shiguang Shan
    • 1
  • Laiyun Qing
    • 2
  • Xilin Chen
    • 1
  • Wen Gao
    • 1
    • 3
  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Digital MediaPeking UniversityBeijingChina

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