Driver Fatigue Detection Using Viola Jones and Principal Component Analysis

In this paper, we have proposed a low-cost solution for driver fatigue detection based on micro-sleep patterns. Contrary to conventional methods, we acquired images by placing a camera on the extreme left side of the driver and proposed two algorithms that facilitate accurate face and eye detections, even when the driver is not facing the camera or driver's eyes are closed. The classification to find whether eye is closed or open is done on the right eye only using SVM and Adaboost. Based on eye states, micro-sleep patterns are determined and an alarm is triggered to warn the driver, when needed. In our dataset, we considered multiple subjects from both genders, having different appearances and under different lightning conditions. The proposed scheme gives 99.9% and 98.7% accurate results for face and eye detection, respectively. For all the subjects, the average accuracy of SVM and Adaboost is 96.5% and 95.4%, respectively.