Cascade AdaBoost Classifiers with Stage Optimization for Face Detection

  • Zongying Ou
  • Xusheng Tang
  • Tieming Su
  • Pengfei Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zongying Ou
    • 1
  • Xusheng Tang
    • 1
  • Tieming Su
    • 1
  • Pengfei Zhao
    • 1
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of EducationDalian University of TechnologyDalianP.R. China

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