The safety of driving is always an important public issue to be concerned about. Especially in the long-distance driving, the driver's consciousness will decline and the possibility of traffic accident will be increased greatly. According to the problem mentioned above, finding an approach to detect whether the driver is drowsy or not is a useful way to avoid the traffic accident. Because the eye state is the key point in the driver drowsiness detection, a new method to recognize the eye state is proposed in this paper. First, the Haar-like features and Adaboost classifiers are utilized to find the face location and the SVM (Support Vector Machine) classifier is utilized to find the eyes locations. Then, the LBP (Local Binary Pattern) features are calculated for the right eye image. Finally, those features are fed to the SVM classifier to recognize the eye state. The experimental results show that the recognition rate of the proposed method is higher than 80% and the effective eye state recognition method proposed in this paper is helpful for driver drowsiness detection.
LBP Haar-like Adaboost SVM