Face Recognition Based Attendance System Using Deep Learning

Abstract

The project "Face Recognition Based Attendance System Using Deep Learning" aims to modernize attendance tracking in educational institutions by incorporating deep learning and facial recognition technologies. Traditional manual attendance systems are often inaccurate, time-consuming, and susceptible to security issues like proxy attendance. This system offers an automated solution by leveraging facial recognition algorithms to identify students and mark their attendance in real time. The web-based application is built using HTML, CSS, JavaScript, PHP, and MySQL to ensure accessibility and functionality. It features an intuitive interface for administrators, lecturers, and students, with functionalities like user authentication, student enrollment, and course management. During enrollment, students’ facial data is captured and stored for future identification. In class, the system uses face-api.js, a JavaScript library built on deep learning algorithms, to process real-time webcam images and automatically mark attendance by recognizing students' faces. Administrators can easily manage faculty records, assign courses, and generate detailed attendance reports, which can be exported to Excel for further analysis. The integration of face-api.js ensures efficient, secure, and real-time face recognition, while the MySQL database manages data storage with a focus on privacy and security. By automating the attendance process, the system reduces administrative workload and enhances accuracy. It also overcomes the limitations of traditional methods, offering a scalable, secure, and user-friendly solution for modern educational environments. This project represents a significant advancement in educational technology, providing institutions with a reliable, real-time system that improves operational efficiency and security in attendance tracking.