Journal of Electronics (China)

, Volume 26, Issue 4, pp 509–516 | Cite as

An LBP-based multi-scale illumination preprocessing method for face recognition

  • Guoxing Jiang
  • Yanfang Cheng


It is one of the major challenges for face recognition to minimize the disadvantage of illumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination preprocessing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recognition rate.

Key words

Face recognition Illumination preprocessing Local Binary Pattern (LBP) 

CLC index



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    O. Lahdenoja, M. Laiho, and A. Paasio. Reducing the feature vector length in local binary patten based face recognition. Proceedings of IEEE International Conference on Image Processing (ICIP’2005), Genoa, Italy, September 11–14, 2005, Vol.2, 914–917.Google Scholar
  2. [2]
    T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2006)12, 2037–2041.CrossRefGoogle Scholar
  3. [3]
    S. C. Liao, X. X. Zhu, Z. Lei, L. Zhang, and S. Z. Li. Learning multi-scale block local binary patterns for face recognition. Proceedings of IAPR/IEEE International Conference on Biometrics (ICB’ 2007), Seoul, Korea, August 27–29, 2007, 828–837.Google Scholar
  4. [4]
    M. Sebastien, R. Yann, and H. Guillaume. On the recent use of local binary patterns for face authentication. International Journal on Image and Video Processing, Special Issue on Facial Image Processing, 2007(2007)1, 1–9.Google Scholar
  5. [5]
    Y. G. Huang, Y. H. Wang, and T. N. Tan. Combining statistics of geometrical and correlative features for 3D face recognition. Proceedings of the 17th British Machine Vision Conference (BMVC’ 2006), Edinburgh, UK, September 4–7, 2006, 879–888.Google Scholar
  6. [6]
    Olli Lahdenoja, Mika Laiho, Janne Maunu, and Ari Paasio1. A massively parallel face recognition system. EURASIP Journal on Embedded Systems, 2007 (2007)1, 1–13.CrossRefGoogle Scholar
  7. [7]
    B. C. Zhang, S. G. Shan, X. L. Chen, and W. Gao. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Transactions on Image Processing, 16(2007)1, 57–68.CrossRefMathSciNetGoogle Scholar
  8. [8]
    G. Zhang, X. Huang, S. Z. Li, Y. Wang, and X. Wu. Boosting local binary pattern (LBP)-based face recognition. Proceedings of the 5th Chinese Conference on Biometric Recognition (SINOBIOMETRICS’ 2004), Guangzhou, China, December 13–15, 2004, 179–186.Google Scholar
  9. [9]
    Y. Adini, Y. Moses, and S. Ullman. Face recognition: the problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1997)7, 721–732.CrossRefGoogle Scholar
  10. [10]
    T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1996)1, 51–59.CrossRefGoogle Scholar
  11. [11]
    T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2002)7, 971–987.CrossRefGoogle Scholar
  12. [12]
    G. Heusch, Y. Rodriguez, and S. Marcel. Local binary patterns as an image preprocessing for face authentication. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FG’2006), Southampton, UK, April 10–12, 2006, 9–14.Google Scholar
  13. [13]
    A. Shashua and T. Riklin-Raviv. The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 123(2001)2, 129–139.CrossRefGoogle Scholar
  14. [14]
    Qian Tao and Raymond Veldhuis. Illumination normalization based on simplified local binary patterns for face verification system. Proceedings of Biometrics Symposium 2007, Baltimore, Maryland, USA, September 11–13, 2007, 1–7.Google Scholar
  15. [15]
    F. Tajeripour, E. Kabir, and A. Sheikhi. Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing, 2008(2008)1, 1–12.CrossRefGoogle Scholar
  16. [16]
    A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2001)6, 643–660.CrossRefGoogle Scholar
  17. [17]
    S. Eickeler, S, Müller, and G. Rigoll. Recognition of JPEG compressed face images based on statistical methods. Image and Vision Computing, 18(2000)4, 279–287.CrossRefGoogle Scholar
  18. [18]
    D. J. Jobson, Z. Rahman, and G. A. Woodell. Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 6(1997)3, 451–462.CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Electronics, CAS and Springer Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations