Efficient Image Appearance Description Using Dense Sampling Based Local Binary Patterns

  • Juha Ylioinas
  • Abdenour Hadid
  • Yimo Guo
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


This work presents a novel image appearance description method based on the highly popular local binary pattern (LBP) texture features. The key idea consists of introducing a dense sampling encoding strategy for extracting more stable and discriminative texture patterns in local regions. Compared to the conventional sparse sampling scheme commonly used in basic LBP, our proposed dense sampling aims to generate, through a form of up-sampling, more neighboring pixels so that more stable LBP codes, carrying out richer information, are computed. This yields in significantly enhanced image description which is less prone to noise and to sparse and unstable histograms. Another interesting property of the dense sampling scheme is that it can be easily integrated with many existing LBP variants. Extensive experiments on three different classification problems namely face recognition, texture classification and age group estimation on various challenging benchmark databases clearly demonstrate the efficiency of the proposed scheme, showing very promising results compared not only to original LBP but also to state-of-the-art especially in the very demanding task of human age estimation.


Face Recognition Local Binary Pattern Dense Sampling Local Binary Pattern Feature Local Binary Pattern Operator 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
  2. 2.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV 1999 (1999)Google Scholar
  3. 3.
    Grigorescu, S., Petkov, N., Kruizinga, P.: Comparison of texture features based on gabor filters. IEEE TIP 11, 1160–1167 (2002)MathSciNetGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005 (2005)Google Scholar
  5. 5.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  6. 6.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE TIP 19, 1657–1663 (2010)Google Scholar
  7. 7.
    Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using lbp variance (lbpv) with global matching. Pattern Recognition 43, 706–719 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Mäenpää, T., Pietikäinen, M.: Multi-scale Binary Patterns for Texture Analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 885–892. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning Multi-scale Block Local Binary Patterns for Face Recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Liao, S., Chung, A.C.S.: Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient Magnitude. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 672–679. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49, 117–125 (2010)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: ICPR 2002, pp. 701–706 (2002)Google Scholar
  13. 13.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics 18, 1–34 (1999)CrossRefGoogle Scholar
  14. 14.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16, 295–306 (1998)CrossRefGoogle Scholar
  15. 15.
    Gallagher, A., Chen, T.: Understanding images of groups of people. In: CVPR 2012, pp. 256–263 (2012)Google Scholar
  16. 16.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Shan, C.: Learning local features for age estimation on real-life faces. In: MPVA 2010, pp. 23–28 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juha Ylioinas
    • 1
  • Abdenour Hadid
    • 1
  • Yimo Guo
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Center for Machine Vision ResearchUniversity of OuluFinland

Personalised recommendations