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3D Cascade of Classifiers for Open and Closed Eye Detection in Driver Distraction Monitoring

  • Mahdi Rezaei
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6855)

Abstract

Eye status detection and localization is a fundamental step for driver awareness detection. The efficiency of any learning-based object detection method highly depends on the training dataset as well as learning parameters. The research develops optimum values of Haar-training parameters to create a nested cascade of classifiers for real-time eye status detection. The detectors can detect eye-status of open, closed, or diverted not only from frontal faces but also for rotated or tilted head poses. We discuss the unique features of our robust training database that significantly influenced the detection performance. The system has been practically implemented and tested in real-world and real-time processing with satisfactory results on determining driver’s level of vigilance.

Keywords

Face Detection Search Window Tilted Head Face Recognition Algorithm FERET Database 
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 2011

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 1
  1. 1.The .enpeda.. ProjectThe University of AucklandAucklandNew Zealand

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