Deep Learning Based Driver Warning System Based on Face Features Recognition

Drowsy driving poses significant risks, accounting for a quarter of car accidents. Involving inattentiveness and unconsciousness, it leads to 1,500 deaths, 71,000 injuries, and $12.5 billion in damages yearly. Drivers in Bangladesh are unwilling to obey the regulations while driving, and the majority of truck drivers are drug addicts. To address this, a continuous alarm system detecting drowsiness is required This research focuses on identifying drowsiness, unconsciousness, and agitation through facial expressions and eye movement analysis. Using deep learning, a camera records and analyzes each frame, alerting unfit drivers. The system integrates facial expressions for enhanced accuracy. Results confirm the system’s high precision, surpassing existing algorithms. Such systems offer a potential solution to mitigate drowsy driving’s grave consequences and improve road safety.