Smart Attendance Monitoring System Using Face Recognition for People with Disabilities (PwDs)

This research presents an innovative and inclusive solution to automate attendance-taking processes in various settings, particularly educational institutions, by introducing a user-friendly GUI-based smart attendance monitoring system utilising facial recognition technology. Addressing the limitations and inefficiencies of traditional methods like RFID cards or manual signing, the proposed system offers an efficient, accurate, and accessible means of registering individuals' attendance, specifically benefiting students with disabilities. Leveraging advanced models like the pre-trained DNN Caffe model for face detection and the Openface model for face recognition, complemented by an SVM classifier and Tkinter for the User Interface (UI), the system achieves an impressive average accuracy of 92% in diverse contextual parameters, including challenging lighting, occlusion, orientation, and background conditions. Notably, the model can recognise side-oriented faces and multiple faces simultaneously, making it robust for real-world environments. This research project contributes to attendance management practices, highlighting the benefits of the developed system while offering potential solutions to overcome its limitations. It serves as a foundation for further research and opens up possibilities for inclusive attendance tracking in diverse contexts beyond education.