Learning Multi-scale Block Local Binary Patterns for Face Recognition

  • Shengcai Liao
  • Xiangxin Zhu
  • Zhen Lei
  • Lun Zhang
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


In this paper, we propose a novel representation, called Multi-scale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.


LBP MB-LBP Face Recognition AdaBoost 


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision, Prague, Czech, pp. 469–481 (2004)Google Scholar
  2. 2.
    Balas, B., Sinha, P.: Toward dissociated dipoles: Image representation via non-local comparisons. In: CBCL Paper #229/AI Memo #2003-018, MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA (August 2003)Google Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Crow, F.: Summed-area tables for texture mapping. SIGGRAPH 18(3), 207–212 (1984)CrossRefGoogle Scholar
  5. 5.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity (July 1998)Google Scholar
  6. 6.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  7. 7.
    Moghaddam, B., Nastar, C., Pentland, A.: A Bayesain similarity measure for direct image matching. Media Lab Tech Report No.393, MIT (August 1996)Google Scholar
  8. 8.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  9. 9.
    Ojala, T., Pietikainen, M., Maenpaa, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  10. 10.
    Penev, P., Atick, J.: Local feature analysis: A general statistical theory for object representation. Neural Systems 7(3), 477–500 (1996)zbMATHCrossRefGoogle Scholar
  11. 11.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  12. 12.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91 (1998)Google Scholar
  13. 13.
    Simard, P.Y., Bottou, L., Haffner, P., Cun, Y.L.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 571–577. MIT Press, Cambridge (1998)Google Scholar
  14. 14.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  15. 15.
    Viola, P., Jones, M.: Robust real time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, July 13, 2001 (2001)Google Scholar
  16. 16.
    Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.v.d.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)CrossRefGoogle Scholar
  17. 17.
    Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 180–187. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Shengcai Liao
    • 1
  • Xiangxin Zhu
    • 1
  • Zhen Lei
    • 1
  • Lun Zhang
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research &, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100080China

Personalised recommendations