Driver’s Drowsiness Detection System Based on Facial Expression Recognition

The advancement in the integration of computer vision and machine learning for solving practical problems of man has motivated various researchers to develop solutions in the domain of drowsy driving using different technological advancements. This paper aims at developing a Driver Drowsiness Detection System to enhance road safety by leveraging facial expression analysis for real-time detection of drowsiness in drivers. It monitors facial cues such as eyelid closure, head nodding, and yawning to detect drowsy driving. The key features of this system include non-intrusiveness, adaptability to varying road conditions, and seamless integration with existing vehicle systems. Through the synergistic application of machine learning algorithms and advanced facial recognition techniques, the system offers robust and timely alerting mechanisms to drivers, for effective prevention of road accidents thereby saving lives. The proposed system was implemented using Python programming language due to its ability to provide platform independence, community support, Machine Learning capabilities, integration with OpenCV, Prototyping and iterative development. The programming language is equally versatile and relatively easy to development software hence providing an ideal environment for implementing the driver drowsiness detection system. The system was evaluated using three test cases and the result shows that the system is very efficient in detecting driver drowsiness status.