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Driver Drowsiness Detection Based on Joint Monitoring of Yawning, Blinking and Nodding

Deaths and injuries resulting from road traffic crashes remain a serious problem globally and current trends suggest that this will continue to be the case in the foreseeable future. In this paper, we propose a reliable method towards drowsiness detection by analyzing images of the driver’s face. In fact, the shadows caused by wearing glasses and/or bad light conditions may, in particular, decrease the accuracy rate of the blinking detection. In addition, drowsiness symptoms are not just restricted to the frequency of blinking, as most of existing works do, but also include yawning and nodding. Thus, instead of using a single facial feature to predict the drowsiness state, we jointly combine eye closure and yawning by measuring the eye and the mouth aspect ratios, and head pose which is estimated by analyzing the optical flow. Then, we investigate the multilayer perceptron and the K-nearest neighbors as two classification techniques. The proposed method has been tested on the challenging private NTHU-DDD benchmark video dataset, and the preliminary experimental results show the effectiveness of the proposed automated method.