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

Precise eye status detection and localization is a fundamental step for driver distraction detection. The efficiency of any learning-based object detection method highly depends on the training dataset as well as learning parameters. The reported 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 a frontal faces but also for rotated or tilted head poses. We discuss the unique specification of our robust training database that significantly influenced the detection performance. The system has successfully been implemented in a research vehicle for real-time and real-world processing with satisfactory results on determining driver’s level of vigilance.