Driver Facial Emotions and Drowsiness Detection for Enhanced Advanced Driver Assistance Systems(ADAS): Safety Management

The proposed approach seeks to recognize real-time emotions from human facial expressions. Facial emotions are the most essential visual indicators for identifying a driver’s emotional state and weariness. The framework work is developed to allow the computer to automatically extract the facial identification of human emotions using a deep face library and the amount of fatigue of eyes calculating eye-aspect-ratio (EAR) with Open CV2. From the results of our study, the model shows that if eyelids remain closed for longer than projected, this is a sign of weariness or drowsiness. A warning signal is provided to the driver if the system finds symptoms of sleepiness straight ahead concerning the driver’s safety on the road. The project proposes to construct a non-intrusive driver warning system as part of Advanced Driver Assistance Systems (ADAS) to increase the safety of all road users while driving.