International Journal of Science, Engineering and TechnologyAuthors: Aditya, Akshat Saxena, Ankit Kumar, Rahul Singh, Shivam Singh Chandel
Abstract: Attendance management plays a significant role in educational institutions for monitoring student participation, maintaining academic records, and ensuring classroom discipline. Conventional attendance systems based on manual entry methods are time-consuming, inefficient, and prone to human errors and proxy attendance. With the rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision technologies, automated attendance systems using facial recognition have become an effective alternative to traditional approaches. This research presents an AI Based Face Recognition Attendance System designed to automate attendance recording through real-time facial recognition techniques. The proposed system integrates Artificial Intelligence with web-based technologies to identify and verify students using webcam input. The system is developed using React.js for frontend development, Flask APIs for face recognition operations, Node.js and Express.js for backend services, and MongoDB for database management. OpenCV and the Local Binary Pattern Histogram (LBPH) algorithm are used for face detection and face recognition processes. The system provides functionalities such as student enrollment, face dataset generation, automated attendance marking, period-wise attendance management, administrator authentication, attendance dashboards, and attendance log monitoring. Facial images captured through webcam devices are processed using OpenCV algorithms and matched against trained facial datasets to recognize students in real time. Once recognition is successful, attendance records are automatically stored in MongoDB collections and displayed through interactive dashboard interfaces. The proposed framework improves attendance accuracy, minimizes manual workload, reduces proxy attendance, and enhances data management efficiency. Experimental analysis demonstrates that the system performs effectively under moderate lighting conditions and provides reliable real-time recognition performance. The developed solution is scalable, user-friendly, cost-effective, and suitable for smart classrooms, universities, and modern educational environments.
