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Statistical Learning of Multi-view Face Detection

  • Stan Z. Li
  • Long Zhu
  • ZhenQiu Zhang
  • Andrew Blake
  • HongJiang Zhang
  • Harry Shum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)

Abstract

A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1,2] is a sequential forward search procedure using the greedy selection strategy. The premise offered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search [3] into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost.

We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320×240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.

Keywords

Support Vector Machine Feature Selection Statistical Learn Face Detection Heuristic Assumption 
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 2002

Authors and Affiliations

  • Stan Z. Li
    • 1
  • Long Zhu
    • 1
  • ZhenQiu Zhang
    • 2
  • Andrew Blake
    • 3
  • HongJiang Zhang
    • 1
  • Harry Shum
    • 1
  1. 1.Microsoft Research AisaBeijingChina
  2. 2.Institute of AutomationChinese Academy SinicaBeijingChina
  3. 3.Microsoft Research CambridgeCambradgeUK

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