Published Paper Details:

Driver drowsiness is a significant cause of road accidents worldwide, making early and accurate detection critical for enhancing road safety. This study presents a novel approach to driver drowsiness detection utilizing facial recognition and advanced image analysis techniques. By employing face recognition technology, the system ensures secure user authentication via a login interface, utilizing the face_recognition library to match face encodings accurately. Following authentication, the system leverages OpenCV and dlib to perform real-time video analysis, calculating the Eye Aspect Ratio (EAR) from facial landmarks to monitor driver alertness. Advanced image processing methods are integrated to improve the accuracy of drowsiness detection, addressing challenges posed by varying facial features and lighting conditions. The system also includes adaptive algorithms to account for different driver demographics and environments, enhancing its versatility and effectiveness. The proposed method is evaluated against existing techniques, demonstrating superior performance in terms of accuracy, robustness, and efficiency. This approach not only enhances the ability to detect driver drowsiness but also contributes to more effective road safety practices, ultimately supporting safer driving and reducing accident rates.