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Feature Type and Size Selection for AdaBoost Face Detection Algorithm

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Image Processing and Communications Challenges 2

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 84))

Summary

The article presents different sets of Haar-like features defined for adaptive boosting (AdaBoost) algorithm for face detection. Apart from a simple set of pixel intensity differences between horizontally or vertically neighboring rectangles, the features based on rotated rectangles are considered. Additional parameter that limits the area on which the features are calculated is also introduced. The experiments carried out on the set of MIT 19×19 face and non-face examples showed the usefulness of particular types of features and their influence on generalization.

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References

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Author information

Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, ul. Narutowicza 11/12, 80-952, Gdansk, Poland

    Jerzy Dembski

Authors
  1. Jerzy Dembski
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Editor information

Editors and Affiliations

  1. University of Technology and Life Sciences, Kordeckiego 20, 85-225, Bydgoszcz, Poland

    Ryszard S. Choraś

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Dembski, J. (2010). Feature Type and Size Selection for AdaBoost Face Detection Algorithm. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 2. Advances in Intelligent and Soft Computing, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16295-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-16295-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16294-7

  • Online ISBN: 978-3-642-16295-4

  • eBook Packages: EngineeringEngineering (R0)

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