Visual-based Real Time Driver Drowsiness Detection System Using CNN

The traffic accident is one of the most frequent cause of death in the world; and an important cause of the traffic accident is the fatigue of the driver, who falls asleep during driving. To overcome this problem in this paper, we propose a real-time driver drowsiness detection system, in which the driver's face region is extracted and introduced into a specific designed shallow convolutional neural network (SS-CNN). The SS-CNN detects the state of driver drowsiness using "eye closure" or "eye open" state. To distinguish between the "eye closed" state caused by normal eye blinking and that caused by drowsiness, the proposed system analyzes consecutive results of the SS-CNN. If the system determines that driver falls asleep, an alarm rings to awake the driver in order to avoid a possible accident. The proposed SS-CNN provides an accuracy of 98.95%, which outperforms the previous works. In the experimental section, we provide several links in which real-time operations of the proposed system can be observed.