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A Binary Decision Tree Implementation of a Boosted Strong Classifier

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Analysis and Modelling of Faces and Gestures (AMFG 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3723))

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Abstract

Viola and Jones [1] proposed the influential rapid object detection algorithm. They used AdaBoost to select from a large pool a set of simple features and constructed a strong classifier of the form {∑ j α j h j (x) ≥ θ} where each h j (x) is a binary weak classifier based on a simple feature. In this paper, we construct, using statistical detection theory, a binary decision tree from the strong classifier of the above form. Each node of the decision tree is just a weak classifier and the knowledge of the coefficients α j is no longer needed. Also, the binary tree has a lot of early exits. As a result, we achieve an automatic speedup that always makes the rapid Viola and Jones algorithm rapider.

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Author information

Authors and Affiliations

  1. Integrated Data Systems Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540

    S. Kevin Zhou

Authors
  1. S. Kevin Zhou
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Editor information

Editors and Affiliations

  1. Intuitive Surgical Inc, 950 Kifer Road, 94086, Sunnyvale, CA, USA

    Wenyi Zhao

  2. Shaogang Gong, Department of Computer Science, Queen Mary, University of London, E1 4NS, London, UK

    Shaogang Gong

  3. Microsoft Research Asia, P.O. Box, Beijing, P.R. China

    Xiaoou Tang

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhou, S.K. (2005). A Binary Decision Tree Implementation of a Boosted Strong Classifier. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_16

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  • DOI: https://doi.org/10.1007/11564386_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29229-6

  • Online ISBN: 978-3-540-32074-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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