Integrating Geofencing and Real-Time Face Recognition Using Machine Learning for Smart Attendance

Traditional attendance systems have struggled to maintain accuracy, security, and efficiency in this regard in recent years. This paper addresses these challenges by proposing using Geofencing and Face Recognition, powered by Deep Learning (DL), for a Smart Attendance System. The system aims to automate attendance tracking by verifying the location and identity of a user at the same time. With geofencing, attendance can only be marked when the user crosses a geographical boundary, be it a school, college, or office. In this paper, the Advanced DL algorithm, such as Convolutional Neural Networks (CNN), uses face recognition to act on this common perception and significantly enhances the identity verification process by recognizing and validating faces in real time with high accuracy. The bounding box model combined with GPS location helps the proposed approach to find the student's accurate location based on the coordinates obtained. Finally, results show that the proposed approach achieved superior performance.