Real-time driver eye detection method using Support Vector Machine with Hu invariant moments

In the development of advanced vehicle safety systems, monitoring the driverpsilas vigilance level and issuing an alert when he is not paying enough attention to the road is a promising way to reduce the road accidents. In such driver monitoring systems, developing a reliable real-time driver eye de...

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Bibliographic Details
Main Authors: Guang-Yuan Zhang, Bo Cheng, Rui-Jia Feng, Jia-Wen Li
Format: Conference Proceeding
Language:English
Subjects:
Driver circuits
Eye detection
Feature extraction
Hu Moment invariant
Lighting
Machine learning
Non-intrusive
Real time systems
Real-time
Support Vector Machine
Support vector machines
Training
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Summary:In the development of advanced vehicle safety systems, monitoring the driverpsilas vigilance level and issuing an alert when he is not paying enough attention to the road is a promising way to reduce the road accidents. In such driver monitoring systems, developing a reliable real-time driver eye detection method is a crucial part. In this paper, a rear-time eye detection method using support vector machine (SVM) with Hu invariant moments is proposed. In the method binarization and heuristic rules to screen the contour are firstly used to find the region of interest (ROI) of the driverpsilas eye. Then the Hu invariant moments of the ROI are calculated and further used in developing the SVM model. The test sets from the experiment were used to validate the classification results. The validation results and conclusions about the performance of the method are presented.
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620921