Driver Drowsiness Detection System with OpenCV and Keras

Every year, many people lose their lives during road accidents caused by drowsy driving around the globe. Drowsy driving, one of the root causes of road accidents is due to tiredness and snoozy eyes while driving. The number of accidents occurring because of drowsy drivers can be decreased by detecting it in real time. This Research paper proposes a Driver Drowsiness Detection System (DDDS) that uses machine learning techniques to detect drowsiness in drivers. The suggested technology employs a camera to record photos of the driver's face and monitor their eye and head movements. These photos are further processed using machine learning algorithms to identify signs of sleepiness, including eye closure, head tilt, and yawning. For real-time driver drowsiness detection, the proposed DDDS using machine learning is a promising solution. It may reduce the number of accidents caused by drowsy driving, thereby enhancing traffic safety. This research paper provides a thorough description of the proposed system, covering its development, application and assessment.