Skip to main content
SpringerLink
Log in

Shadow compensation and illumination normalization of face image

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This study proposes a novel shadow compensation and illumination normalization method under uncontrolled light conditions. First, we decompose the face image into two images based on the Lambertian theory, which corresponds to the large- and small-scale features, respectively. Then, the threshold minimum-and-maximum filter on the small-scale features to smooth the shadow edge is applied. After that, the robust Principal Component Analysis and some normalization methods are used to remove the shadow and normalize the face image on the large-scale features. In the end, the normalized face image is obtained by combining both results from the large- and small-scale features. Our main contribution is that a more reliable shadow compensation approach is found, which can get a better normalized face image. Experiments on the Extended Yale B, CMU-PIE and FRGC 2.0 (Face Recognition Grand Challenge) face datasets show that not only the recognition performance is significantly improved, but also much better visual quality is achieved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: ECCV, pp. 469–481 (2004)

  2. Al-Osaimi, F.R., Bennamoun, M., Mian, A.: Illumination normalization of facial images by reversing the process of image formation. Mach. Vis. Appl. 22(6), 899–911 (2011)

    Article  Google Scholar 

  3. Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Analysis Mach. Intell. 25(2), 218–233 (2003)

    Article  Google Scholar 

  4. Belhumeur, P.N., Kriegman, D.J.: What is the set of images of an object under all possible illumination conditions? Int. J. Comput. Vis. 28(3), 245–260 (1998)

    Article  Google Scholar 

  5. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), Article 11 (2011)

    Google Scholar 

  6. Chang, H., Koschan, A., Abidi, B., Abidi, M.: Fusing continuous spectral images for face recognition under indoor and outdoor illuminants. Mach. Vis. Appl. 21(2), 201–215 (2010)

    Article  Google Scholar 

  7. Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. Pattern Analysis Mach. Intell. 28(9), 1519–1524 (2006)

    Article  Google Scholar 

  8. Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. B Cybern. 36(2), 458–466 (2006)

    Article  Google Scholar 

  9. Garcia, C., Zikos, G., Tziritas, G.: A wavelet-based framework for face recognition, In: International Workshop on Advances in Facial Image Analysis and Recognition Technology, 5th European Conference on Computer Vision (1998)

  10. 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. Pattern Analysis Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  11. Hafed, Z.M., Levine, M.D.: Face recognition using the discrete cosine transform. Int. J. Comput. Vis. 43(3), 167–188 (2001)

    Article  MATH  Google Scholar 

  12. Kumar, R., Jones, M., Marks, T.K.: Morphable reflectance fields for enhancing face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2606–2613 (2010)

  13. Lai, J.H., Yuen, P.C., Feng, G.C.: Face recognition using holistic Fourier invariant features. Pattern Recognit. 34(1), 95–109 (2001)

    Article  MATH  Google Scholar 

  14. Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)

    Google Scholar 

  15. Li, D., Tang, X., Pedrycz, W.: Face recognition using decimated redundant discrete wavelet transforms. Mach. Vis. Appl. 23(2), 1–11 (2012)

    Google Scholar 

  16. Phillips, P.J., Flynn, P.J., Scruggs, T.: Overview of the face recognition grand challenge. In: IEEE conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)

  17. Shashua, A., Riklin-Raviv, T.: The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans. Pattern Analysis Mach. Intell. 23(2), 129–139 (2001)

    Article  Google Scholar 

  18. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Analysis Mach. Intell. 25(12), 1615–1618 (2003)

    Article  Google Scholar 

  19. Vizireanu, N.D.: Generalizations of binary morphological shape decomposition. J. Electron. Imaging 16(1), 1–6 (2007)

    Article  Google Scholar 

  20. Vizireanu, D.N.: Morphological shape decomposition interframe interpolation method. J. Electron. Imaging 17, 1–5 (2008)

    Article  Google Scholar 

  21. Vizireanu, N., Halunga, S., Marghescu, G.: Morphological skeleton decomposition interframe interpolation method. J. Electron. Imaging 19, 1–3 (2010)

    Article  Google Scholar 

  22. Wang, H., Li, S.Z., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 819–824 (2004)

  23. Wang, H., Li, S.Z., Wang, Y.: Generalized quotient image. In: IEEE Computer Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 498–505 (2004)

  24. Wright, J., Ganesh, A., Rao, S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Conference on Neural Information Processing Systems (2009)

  25. Xie, X., Zheng, W.S., Lai, J.: Face illumination normalization on large and small scale features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

  26. Xie, X., Zheng, W.S., Lai, J., Yuen, P.C., Suen, C.Y.: Normalization of face illumination based on large- and small-scale features. IEEE Trans. Image Process. 20(7), 1807–1821 (2011)

    Article  MathSciNet  Google Scholar 

  27. Zhang, T., Fang, B., Yuan, Y., Yan Tang, Y., Shang, Z., Li, D., Lang, F.: Multiscale facial structure representation for face recognition under varying illumination. Pattern Recognit. 42(2), 251–258 (2009)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the authors in [7] and [26] for offering the code of LTV model and the authors in [5, 24] for offering the robust PCA code. We especially thank the authors in [26] and [8] for mindful guidance and kindly help. This work was supported in part by the 973 National Basic Research Program of China (2010CB732501), Fundation of Sichuan Excellent Young Talents (09ZQ026-035) and Fundamental Research Funds for the Central University.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Haiyang Wang, Mao Ye & Shangming Yang

Authors
  1. Haiyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mao Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shangming Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mao Ye.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, H., Ye, M. & Yang, S. Shadow compensation and illumination normalization of face image. Machine Vision and Applications 24, 1121–1131 (2013). https://doi.org/10.1007/s00138-013-0488-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0488-y

Keywords

Search

Navigation