Hybrid Student Authentication System Using RFID Reader and Face Biometrics Using Deep Learning Techniques

Abstract

Every school, college, and university maintains a record of each student's attendance. Faculty are required to retain accurate and current attendance records. Because it takes a long time to organize records and determine each student's average attendance, the manual attendance record system is inefficient. As a result, a system for organizing student records and calculating average attendance is required The proposed system should be able to save student attendance records in digital format, making attendance management easier. Even in the twenty-firs t century, students' attendance is recorded on attendance forms presented in the classroom by staff members, which takes time and is completely manual. Despite the fact that RFID-based and face recognition-based systems have been shown, they are implemented separately. A RFID reader is integrated with a Face recognition system in the suggested system for student attendance. RFID readers, as well as facial recognition cameras, would be placed throughout campus and in classes. When a student walks onto campus, the reader communicates their id to the server, allowing them to be easily tracked After that, using a deep learning approach known as HAAR Cascade and Neural network algorithm, recognize the face from a real-time camera and match it to a database. This strategy ensures that the attendance records of the pupils are stored correctly and efficiently. As a result, the system will generate a list of kids who have been assigned to detention. It's a small-scale automated programme that's easy to operate, redeemable in time, and dependable.