Real-time vision-based eye state detection for driver alertness monitoring
This paper presents a real-time vision-based system to detect the eye state. The system is implemented with a consumer-grade computer and an uncalibrated web camera with passive illumination. Previously established similarity measures between image regions, feature selection algorithms, and classifiers have been applied to achieve vision-based eye state detection without introducing a new methodology. From many different extracted data of 1,293 pair of eyes images and 2,322 individual eye images, such as histograms, projections, and contours, 186 similarity measures with three eye templates were computed. Two feature selection algorithms, the \( J_{5} (\xi ) \) criterion and sequential forward selection, and two classifiers, multi-layer perceptron and support vector machine, were intensively studied to select the best scheme for pair of eyes and individual eye state detection. The output of both the selected classifiers was combined to optimize eye state monitoring in video sequences. We tested the system with videos with different users, environments, and illumination. It achieved an overall accuracy of 96.22%, which outperforms previously published approaches. The system runs at 40fps and can be used to monitor driver alertness robustly.
eye state detection driver alertness monitoring feature selection multi-layer perceptron (mlp) support vector machine (svm)