Facial Recognition and Machine Learning-based Student Attendance Monitoring System
Namrata Narkhede, Anupama Menon, Itisha Mathane, Sayali Nikam, Suchita Dange
Abstract
AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recognition, the system makes it easier for teachers to keep track of students' attendance while also giving administrators useful information. The technology automatically logs student attendance, doing away with the necessity for manual human attendance taking. Each student's attendance rate is determined, and the attendance analysis is made available. Administrators can more easily identify students with strong or poor attendance records thanks to the visualization module of the system, which presents attendance data as a graphical representation. Administrators can use this function to ensure that students attend classes on a regular basis. Additionally, the system notifies students and their parents through email when a particular level of attendance is not met, encouraging students to improve their attendance by taking appropriate action and fostering accountability. The AI-based student monitoring system is a useful tool for teachers and students. It helps to raise academic attainment by promoting an accountability and attendance culture in organizations and institutions. The system also provides administrators with useful data and minimizes the effort for educators.