Towards the Optimal Training of Cascades of Boosted Ensembles

  • S. Charles Brubaker
  • Jianxin Wu
  • Jie Sun
  • Matthew D. Mullin
  • James M. Rehg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)


Cascades of boosted ensembles have become a popular technique for face detection following their introduction by Viola and Jones. Researchers have sought to improve upon the original approach by incorporating new techniques such as alternative boosting methods, feature sets, etc. We explore several avenues that have not yet received adequate attention: global cascade learning, optimal ensemble construction, stronger weak hypotheses, and feature filtering. We describe a probabilistic model for cascade performance and its use in a fully-automated training algorithm.


False Positive Rate Operating Point False Negative Rate Object Detection Face Detection 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Charles Brubaker
    • 1
  • Jianxin Wu
    • 1
  • Jie Sun
    • 1
  • Matthew D. Mullin
    • 1
  • James M. Rehg
    • 1
  1. 1.Georgia Institute of TechnologyCollege of Computing and GVU CenterAtlantaUSA

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