Face-recognition based attendance authentication system

Abstract

Attendance in a classroom lecture can be monitored as a means of tracking student participation. However, the current method of recording attendance using paper-based systems and QR code scanners has proven to be unstable, inefficient, and time-consuming, especially in large classrooms. Differentiating between absentees and proxy attendees becomes challenging with the traditional approach. To address these issues, a web-based attendance system with facial recognition capabilities has been developed. This system integrates an existing web camera with a facial recognition system, enabling automatic and efficient attendance tracking with minimal setup requirements. Through this web application, lecturers have the option to reject student attendance if they violate rules and regulations, and they can also generate attendance reports. Similarly, students can view their attendance percentage for each registered course. Additionally, this research proposes the integration of smart contract-based blockchain technology into the system. In conclusion, this system aims to assist lecturers in monitoring student attendance by utilizing facial recognition technology with an accuracy rate of 98%, while the proposed smart contract ensures secure and efficient management of attendance records.