by Rezaei, M and Klette, R
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. © 2011 Springer-Verlag.
Reference:
3D cascade of classifiers for open and closed eye detection in driver distraction monitoring (Rezaei, M and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6855 LNCS, 2011.
Bibtex Entry:
@inproceedings{rezaei20113dmonitoring,
author = "Rezaei, M and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "171--179",
title = "3D cascade of classifiers for open and closed eye detection in driver distraction monitoring",
volume = "6855 LNCS",
year = "2011",
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. © 2011 Springer-Verlag.",
doi = "10.1007/978-3-642-23678-5_19",
isbn = "9783642236778",
issn = "0302-9743",
eissn = "1611-3349",
issue = "PART 2",
language = "eng",
}