Smart Attendance Monitoring System Using Facial Recognition

This paper presents the design and implementation of a Smart Attendance Monitoring System using Facial Recognition. The system aims to automate the attendance process in educational institutions by identifying students through facial features captured via a camera, eliminating the need for manual or biometric-based attendance. The proposed system uses image processing and machine learning techniques to detect and recognize faces in real time. Once a face is recognized, the corresponding student’s attendance is marked automatically and stored in a database along with the date and time. The system also provides features such as attendance reports, real-time monitoring, and secure data storage. The main objective of this work is to improve accuracy, reduce proxy attendance, and save time compared to traditional attendance methods. The system is suitable for use in classrooms, laboratories, and other institutional environments where reliable and fast attendance tracking is required. This work demonstrates how facial recognition technology can be effectively applied in academic environments to enhance efficiency and data reliability.