Demographic Classification with Local Binary Patterns

  • Zhiguang Yang
  • Haizhou Ai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


LBP (Local Binary Pattern) as an image operator is used to extract LBPH (LBP histogram) features for texture description. In this paper, we present a novel method to use LBPH feature in ordinary binary classification problem. Given a restricted local patch, the Chi square distance between the extracted LBPH and a reference histogram is used as a measure of confidence belonging to the reference class, and an optimal reference histogram is obtained by iteratively optimization; real AdaBoost algorithm is used to learn a sequence of best local features iteratively and combine them into a strong classifier. The experiments on age, gender and ethnicity classification demonstrate its effectiveness.


real AdaBoost LBPH demographic classification 


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: ECCV, pp. 469–481 (May 2004)Google Scholar
  2. 2.
    Feng, X., Cui, J., Pietikainen, M., Hadid, A.: Real time facial expression recognition using local binary patterns and linear programming. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 328–336. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Gutta, S., Wechsler, H., Phillips, P.J.: Gender and ethnic classification of face images. In: FG, pp. 194–199 (April 1998)Google Scholar
  4. 4.
    Hosoi, S., Takikawa, E., Kawade, M.: Ethnicity estimation with facial images. In: FGR, pp. 195–200 (May 2004)Google Scholar
  5. 5.
    Huang, C., Ai, H., Lao, S.: Boosting nested cascade detector for multi-view face detection. In: ICPR, pp. 415–418 (August 2004)Google Scholar
  6. 6.
    Huang, X., Li, S.Z., Wang, Y.: Jensen-shannon boosting learning for object recognition. In: CVPR, pp. 144–149 (June 2005)Google Scholar
  7. 7.
    Kwon, Y.H., da Vitoria Lobo, N.: Age classification from facial images. Computer Vision and Image Understanding 74(1), 1–21 (1999)CrossRefGoogle Scholar
  8. 8.
    Lu, X., Chen, H., Jain, A.K.: Multimodal facial gender and ethnicity identification. In: ICB, pp. 554–561 (January 2006)Google Scholar
  9. 9.
    Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)CrossRefGoogle Scholar
  10. 10.
    Ojala, T., Pietikainen, M., Maenpaa, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  11. 11.
    Phillips, P., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. PAMI 22(10), 1090–1104 (2000)Google Scholar
  12. 12.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)zbMATHCrossRefGoogle Scholar
  13. 13.
    Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression (pie) database. In: FGR, pp. 53–58 (May 2002)Google Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distribution. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  15. 15.
    Wu, B., Ai, H., Huang, C.: Lut-based adaboost for gender classification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 104–110. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhiguang Yang
    • 1
  • Haizhou Ai
    • 1
  1. 1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084China

Personalised recommendations