Face Detection by Learned Affine Correspondences

  • Miroslav Hamouz
  • Josef Kittler
  • Jiri Matas
  • Petr Bílek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

We propose a novel framework for detecting human faces based on correspondences between triplets of detected local features and their counterparts in an affine invariant face appearance model. The method is robust to partial occlusion, feature detector failure and copes well with cluttered background. Both the appearance and configuration probabilities are learned from examples. The method was tested on the XM2VTS database and a limited number of images with cluttered background with promising results — 2% false negative rate — was obtained.

Keywords

Face Feature False Negative Rate Feature Detector Face Detection Appearance Model 
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 2002

Authors and Affiliations

  • Miroslav Hamouz
    • 1
  • Josef Kittler
    • 1
  • Jiri Matas
    • 1
  • Petr Bílek
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyUK

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