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Efficient Face Detection by a Cascaded Support Vector Machine Using Haar-Like Features

  • Matthias Rätsch
  • Sami Romdhani
  • Thomas Vetter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

In this paper, we present a novel method for reducing the computational complexity of a Support Vector Machine (SVM) classifier without significant loss of accuracy. We apply this algorithm to the problem of face detection in images. To achieve high run-time efficiency, the complexity of the classifier is made dependent on the input image patch by use of a Cascaded Reduced Set Vector expansion of the SVM. The novelty of the algorithm is that the Reduced Set Vectors have a Haar-like structure enabling a very fast SVM kernel evaluation by use of the Integral Image. It is shown in the experiments that this novel algorithm provides, for a comparable accuracy, a 200 fold speed-up over the SVM and an 6 fold speed-up over the Cascaded Reduced Set Vector Machine.

Keywords

Support Vector Machine Input Image Face Detection Image Patch Integral Image 
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 2004

Authors and Affiliations

  • Matthias Rätsch
    • 1
  • Sami Romdhani
    • 1
  • Thomas Vetter
    • 1
  1. 1.Computer Science DepartmentUniversity of BaselBaselSwitzerland

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