Driving fatigue is one of the most important factors in trafficaccidents. In this paper,we proposed an improved strategy and practical\nsystem to detect driving fatigue based on machine vision and Adaboost algorithm. Kinds of face and eye classifiers are well trained\nby Adaboost algorithm in advance. The proposed strategy firstly detects face efficiently by classifiers of front face and deflected face.\nThen, candidate region of eye is determined according to geometric distribution of facial organs. Finally, trained classifiers of open\neyes and closed eyes are used to detect eyes in the candidate region quickly and accurately. The indexes which consist of PERCLOS\nand duration of closed-state are extracted in video frames real time.Moreover, the system is transplanted into smart device, that is,\nsmartphone or tablet, due to its own camera and powerful calculation performance. Practical tests demonstrated that the proposed\nsystem can detect driver fatigue with real time and high accuracy. As the system has been planted into portable smart device, it\ncould be widely used for driving fatigue detection in daily life.
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