Face Gender Classification on Consumer Images in a Multiethnic Environment

  • Wei Gao
  • Haizhou Ai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

In this paper, we target at face gender classification on consumer images in a multiethnic environment. The consumer images are much more challenging, since the faces captured in the real situation vary in pose, illumination and expression in a much larger extent than that captured in the constrained environments such as the case of snapshot images. To overcome the non-uniformity, a robust Active Shape Model (ASM) is used for face texture normalization. The probabilistic boosting tree approach is presented which achieves a more accurate classification boundary on consumer images. Besides that, we also take into consideration the ethnic factor in gender classification and prove that ethnicity specific gender classifiers could remarkably improve the gender classification accuracy in a multiethnic environment. Experiments show that our methods achieve better accuracy and robustness on consumer images in a multiethnic environment.

Keywords

Boosting tree gender classification multiethnic environment 

References

  1. 1.
    Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: SEXNET: A Neural Network Identifies Sex from Human Faces. In: NIPS 1990 (1990) Google Scholar
  2. 2.
    Gutta, S., Wechsler, H., Phillips, P.J.: Gender and Ethnic Classification of Face Images. In: FG 1998 (1998) Google Scholar
  3. 3.
    Balci, K., Atalay, V.: PCA for Gender Estimation: Which Eigenvectors Contribute? In: ICPR 2002 (2002) Google Scholar
  4. 4.
    Moghaddam, B., Yang, M.H.: Learning Gender with Support Faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5) (May 2002) Google Scholar
  5. 5.
    Hayashi, J., Yasumoto, M., Ito, H., Koshimizu, H.: Age and Gender Estimation based on Wrinkle Texture. In: ICPR 2002 (2002) Google Scholar
  6. 6.
    BenAbdelkader, C., Griffin, P.: A Local Region-based Approach to Gender Classification from Face Images. In: CVPR 2005 (2005) Google Scholar
  7. 7.
    Yang, Z., Li, M., Ai, H.: An Experimental Study on Automatic Face Gender Classification. In: ICPR 2006 (2006) Google Scholar
  8. 8.
    Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A Unified Learning Framework for Real Time Face Detection and Classification. In: AFG 2002 (2002) Google Scholar
  9. 9.
    Lapedriza, A., Masip, D., Vitrià J.: Are External Face Features Useful for Automatic Face Classification. In: CVPR 2005 (2005) Google Scholar
  10. 10.
    Lapedriza, A., Manuel, M.J., Jiménez, J.M., Vitrià, J.: Gender Recognition in Non Controlled Environments. In: ICPR 2006 (2006) Google Scholar
  11. 11.
    Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: ICCV 2005 (2005) Google Scholar
  12. 12.
    Huang, C., Ai, H., Wu, B., Lao, S.: Boosting nested cascade detector for multi-view face detection. In: ICPR 2004 (2004) Google Scholar
  13. 13.
    Zhang, L., Ai, H., Xin, S., Huang, C., Tsukiji, S., Lao, S.: Robust Face Alignment Based on Local Texture Classifiers. In: ICIP 2005 (2005) Google Scholar
  14. 14.
    Wu, B., Ai, H., Huang, C.: LUT-Based AdaBoost for Gender Classification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688. Springer, Heidelberg (2003) Google Scholar
  15. 15.
    Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning 37, 297–336 (1999) Google Scholar
  16. 16.
    Viola, P., Jones, M.: Fast Multi-view Face Detection. In: Proc. of CVPR (2001) Google Scholar
  17. 17.
    Gutta, S., Huang, J.R., Jonathon, P., Wechsler, H.: Mixture of Experts for Classification of Gender, Ethnic Origin, and Pose of Human Faces. IEEE Transactions on Neural Networks Google Scholar
  18. 18.
    Yang, Z., Ai, H.: Demographic classification with local binary patterns. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 464–473. Springer, Heidelberg (2007) Google Scholar
  19. 19.
    Hosoi, S., Takikawa, E., Kawade, M.: Ethnicity Estimation with Facial Images. In: FG 2004 (2004) Google Scholar
  20. 20.
    Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wei Gao
    • 1
  • Haizhou Ai
    • 1
  1. 1.Computer Science and Technology DepartmentTsinghua UniversityBeijingChina

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