3D cascade of classifiers for open and closed eye detection in driver distraction monitoring

Authors:
Mahdi Rezaei;Reinhard Klette
Affiliations:
The .enpeda.. Project, The University of Auckland, Tamaki Innovation Campus, Auckland, New Zealand;The .enpeda.. Project, The University of Auckland, Tamaki Innovation Campus, Auckland, New Zealand
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

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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 Haartraining 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.