Automated Attendance Monitoring in Buses Using Facial Recognition (Optimized for Moving Environment)

The increasing demand for intelligent public transport systems calls for automated solutions to manage passenger attendance and ensure safety. This paper presents an Automated Attendance Monitoring System for buses using facial recognition technology, specifically optimized for moving environments. The proposed system captures real-time facial images of passengers using cameras mounted at bus entrances and matches them with pre-stored database images through deep learning-based facial recognition algorithms. Unlike traditional attendance systems that require manual input or RFID cards, this approach ensures non-intrusive, fast, and accurate identification even under dynamic lighting and motion conditions. The paper focuses on techniques such as motion compensation, frame stabilization, and face tracking to enhance detection accuracy while the bus is in motion. Experimental results show that the system achieves an accuracy of over 92% in varying environments, making it a reliable and scalable solution for smart transportation networks.