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)


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.


Support Vector Machine Input Image Face Detection Image Patch Integral Image 
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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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