LUT-Based Adaboost for Gender Classification

  • Bo Wu
  • Haizhou Ai
  • Chang Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithms take simple threshold weak classifiers, which are too weak to fit complex distributions, as the hypothesis space. Because of this limitation of the hypothesis model, the training procedure is hard to converge. This paper presents a novel Look Up Table (LUT) weak classifier based Adaboost approach to learn gender classifier. This algorithm converges quickly and results in efficient classifiers. The experiments and analysis show that the LUT weak classifiers are more suitable for boosting procedure than threshold ones.


gender classification Adaboost 


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  1. [1]
    Golomb, D. T. Lawrence, and T. J. Sejnowski. SEXNET: A neural network identifies sex from human faces. In Advances in Neural Information Processing Systems, pp. 572–577, 1991.Google Scholar
  2. [2]
    G. W. Cottrell and J. Metcalfe. EMPATH: Face, emotion, and gender recognition using holons. In Advances in Neural Information Processing Systems, pp. 564–571, 1991.Google Scholar
  3. [3]
    Edelman, D. Valentin, H. Abdi. Sex classification of face areas: how well can a linear neural network predict human performance. Journal of Biological System, Vol. 6(3), pp. 241–264, 1998.CrossRefGoogle Scholar
  4. [4]
    Alice J.O’Toole et al. The Perception of Face Gender: The Role of Stimulus Structure in Recognition and Classification. Memory and Cognition, Vol. 26, pp. 146–160, 1997.Google Scholar
  5. [5]
    Alice J.O’Toole, Thomas Vetter, et al. The role of shape and texture information in sex classification. Technical Report No.23, 1995.Google Scholar
  6. [6]
    Moghaddam and M.H. Yang. Gender Classification with Support Vector Machines. IEEE Trans. on PAMI, Vol. 24, No. 5, pp. 707–711, May 2002Google Scholar
  7. [7]
    G. Shakhnarovich, P.. Viola and B. Moghaddam. A Unified Learning Framework for Real Time Face Detection and Classification. IEEE conf. on AFG 2002.Google Scholar
  8. [8]
    Y. Freund and R. E. Schapire. Experiments with a New Boosting Algorithm. In Proceedings of the 13-th International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann, 1996.Google Scholar
  9. [9]
    P. Viola, M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, in CVPR2001.Google Scholar
  10. [10]
    S. Z. Li, Y. ShiCheng, H. Zhang, Q. Cheng, Multi-View Face Alignment Using Direct Appearance Models, IEEE conf. on AFG 2002.Google Scholar
  11. [11]
    P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, The FERETdatabase and evaluation procedure for face recognition algorithms, Image and Vision Computing J, Vol. 16, No. 5, pp 295–306, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Bo Wu
    • 1
  • Haizhou Ai
    • 1
  • Chang Huang
    • 1
  1. 1.Computer Science and Technology DepartmentTsinghua UniversityBeijingP R China

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