The increasing number of traffic accidents is principally caused by fatigue. In fact, the fatigue presents a real danger on road since it reduces driver capacity to react and analyze information. In this paper we propose an efficient and nonintrusive system for monitoring driver fatigue using yawning extraction. The proposed scheme uses face extraction based support vector machine (SVM) and a new approach for mouth detection, based on circular Hough transform (CHT), applied on mouth extracted regions. Our system does not require any training data at any step or special cameras. Some experimental results showing system performance are reported. These experiments are applied over real video sequences acquired by low cost web camera and recorded in various lighting conditions. 1. Introduction The increasing number of traffic accidents due to a diminished driver’s vigilance level has become a serious problem for society. Statistics show that 20% of all the traffic accidents are due to drivers with a diminished vigilance level [1]. Furthermore, accidents related to driver hypovigilance are more serious than other types of accidents, since hypovigilant drivers do not take correct action prior to a collision. The active safety research focuses on studying the prevention of such accidents by developing systems for monitoring vigilance level and alerting the driver when he is not paying attention to the road. Hypovigilance can be generally identified by sensing physiological characteristics, driver operations, or vehicle responses or monitoring driver’s responses. Among these methods, the techniques based on human physiological phenomena are the most accurate. These techniques are implemented in two ways: measuring changes in physiological signals, such as brain waves, heart rate, and eye blinking and measuring physical changes such as the driver’s head pose and the state of the eyes or the mouth. The first technique, while being most accurate, is not realistic since sensing electrodes would be attached directly on the driver’s body and hence would be annoying and distracting. In addition, long time driving would result in perspiration on the sensors, diminishing their ability to monitor accurately. The techniques based on measuring physical changes are nonintrusive and more suitable for real world driving conditions since they use video cameras to detect changes. Eye state analysis [2–6], head pose estimation [7, 8], and mouth state analysis [9, 10] are the most relevant physical changes allowing the detection of driver’s hypovigilance. Driver operations and
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