Automated Classroom Attendance using a Machine Learning-Based Recognition System

Manually tracking classroom attendance, an entrenched traditional method, presents significant challenges due to its susceptibility to errors and inefficiencies. These limitations not only consume valuable faculty time but also compromise the accuracy of academic records, affecting the evaluation of student engagement and performance. In response to this problem, we present an approach for automated classroom attendance using an embedded machine learning-based recognition system. This research strives to improve the accuracy, efficiency, and reliability of attendance tracking in educational settings. The heart of our research lies in the design and implementation of the system, clarifying the architecture, data flow, and integration into the classroom environment. The results of our analysis show the system's ability to track attendance while providing accurate information on its performance metrics. We also delve into the ethical and practical considerations of implementing such technology in the classroom. By automating the process using machine learning-based recognition, educational institutions can improve their operational efficiency, reduce errors, and ultimately provide a more productive learning environment. Our study opens the door to future avenues of research and technological advances in education.