On Asymmetric Classifier Training for Detector Cascades

  • Timothy F. Gee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


This paper examines the Asymmetric AdaBoost algorithm introduced by Viola and Jones for cascaded face detection. The Viola and Jones face detector uses cascaded classifiers to successively filter, or reject, non-faces. In this approach most non-faces are easily rejected by the earlier classifiers in the cascade, thus reducing the overall number of computations. This requires earlier cascade classifiers to very seldomly reject true instances of faces. To reflect this training goal, Viola and Jones introduce a weighting parameter for AdaBoost iterations and show it enforces a desirable bound. During their implementation, a modification to the proposed weighting was introduced, while enforcing the same bound. The goal of this paper is to examine their asymmetric weighting by putting AdaBoost in the form of Additive Regression as was done by Friedman, Hastie, and Tibshirani. The author believes this helps to explain the approach and adds another connection between AdaBoost and Additive Regression.


Training Sample Loss Function Additive Regression False Acceptance Rate Bias Weighting 
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.
    Viola, P., Jones, M.: Robust real-time object detection. In: ICCV Workshop on Statistical and Computational Theories of Vision - Modeling, Learning, Computing, and Sampling. IEEE, Los Alamitos (2001)Google Scholar
  2. 2.
    Li, S.Z., Zhu, L., Zhang, Z., Zhang, H.: Learning to detect multi-view faces in real-time. In: Proceedings of the IEEE International Conference on Development and Learning (2002)Google Scholar
  3. 3.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the IEEE International Conference onImage Processing, vol. 1, pp. 900–903 (2002)Google Scholar
  4. 4.
    Wu, J., Mullin, M.D., Rehg, J.M.: Linear asymmetric classifier for cascade detectors. In: ICML 2005: Proceedings of the 22nd international conference on Machine learning, pp. 988–995. ACM Press, New York (2005)CrossRefGoogle Scholar
  5. 5.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. NIPS 14 (2002)Google Scholar
  6. 6.
    Healy, M., Ravindran, S., Anderson, D.: Effects of varying parameters in asymmetric adaboost on the accuracy of a cascade audio classifier. In: Proceedings of SoutheastCon., 2004, pp. 169–172. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting (revised with discussions). The Annals of Statistics 28, 337–407 (2000)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Timothy F. Gee
    • 1
  1. 1.Oak Ridge National Laboratory 

Personalised recommendations