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Image-based driver fatigue detection remains a challenging problem due to occlusion of face, the variation of head poses and illuminations. This paper implements an effectual technique for investigating the driver’s fatigue state by using infrared image of an eye in the open or closed condition. In this method we use the deep learning technique to monitor the change, i.e., open and closed conditions of eye state. We integrate ResNet and depthwise convolution network together and use as the core of the structure of the network to perform face and facial landmark detection tasks. After acquiring the eye region, we perform the eye state identification task by using its coordinates of feature points. To determine fatigue, we use PERCLOS method and the results confirm accuracy and effectiveness of the algorithm by comparing with other existing methods. We can reach an accuracy of 97.2% and the average time is 31.20 milliseconds, represent that this driver monitoring inference system has significant importance for both the traffic and driver’s safety.

Detection of Driver Fatigue State using Deep Neural Network Noreen Anwar,Gang Xiong,Miao Guo,Peijun Ye,Hub Ali,Qinglai Wei (corresponding author)