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.

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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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