Driver Fatigue Detection System Based On Facial Features

With the rapid development of transport, the traffic safety has become an important issue be commonly concerned on a global scale. However, each year the figure of people killed in traffic accidents is remaining obstinately high, and all kinds of vicious traffic accidents are still occur. Study shows that most of them are caused by the drivers'fatigue, so driver fatigue has become a hazard to personal safety. Therefore, the study and design of safety assist systems specific to driver fatigue driving attracted much attention of researchers, and become a hotspot all over the world.For the problem, the paper select the facial features of driver as the judgement method to detect the driver fatigue and give timely warning. The main work done in this paper is as follows:Firstly, a method of eye localization and detection based on multimode template matching in projection subspace of local gabor derection under multi-pose condition is presented. The face region is obtained from input color image by AdaBoost algorithm of face detection firstly; Secondly,lighting compensation in face region is done to eliminate the effects of illumination,then the lip is detected according to the lips color, next the left and right corners are detected by harris corner detection algorithm, and the perpendicular bisector of the corners is the symmetry of the face, then the deflection angle of the face is estimated. Finally, gabor transform is used in the corrected face, and local gray projection is performed to locate the rough areas of eyes, then using the multimode template matching in the areas to locate the eyes and judge the state of eyes.Secondly, by analyzing the traditional Mean Shift algorithm, a new approach based on LBP descriptor embedded Mean Shift algorithm for eye and mouth fatigue state tracking is presented in the thesis. The texture features of local binary pattern (LBP) and the color features are combined with together effectively to describe the human eye and embeded Mean Shift algorithm for realizing eye tracking.Finally, the edge image is got by Canny operator from the original image of eye, and then we detecte the blink by measuring the distance of the upper and lower eyelids, next the lip is detected according to the lips color, and the state of mouth is described by the aspect ratio of the lip. In order to make the algorithm more valid, we combined PERCLOS with yawning detection to detect driver's drowsy state, if yawning is detected at the same time, the driver will be judged to fatigue. Experimental results show that the proposed algorithm is effective and feasible, and robust to variations of background, illumination and pose.