Virtual Logger: A Cognitive Framework for Proxy-Proof Attendance Automation and Intelligent Presence Logging

Abstract

Attendance management is an essential activity in educational and other environments. Conventional attendance management systems using manual attendance sheets, RFID cards, or even biometric methods like fingerprints face problems of accuracy, proxy attendance, hygiene issues, and higher maintenance costs. To overcome these problems, this paper proposes an intelligent attendance management system using virtual loggers with face recognition technology. In this paper, we propose an attendance management system using face recognition technology with the help of YOLOv8 face detection and FaceNet face recognition. The system is implemented using a Flask-based web server with a React-based web interface. SQLite is used to store attendance data. One of the major advantages of this system is that it includes an automated parent notification system. Parents would be immediately notified of their child’s attendance or absence. In this paper, we have also compared the accuracy of our system with other conventional attendance management systems. From the results, it is clear that our system is more accurate and scalable compared to other attendance management systems. It is also immune to proxy attendance. Hence, this system is very efficient and reliable.

Introduction

Monitoring attendance is crucial for maintaining discipline in educational institutions and organizations. Traditional methods like registers, RFID cards, and fingerprint scanners are simple but prone to proxy attendance, hygiene issues, high costs, and lack of real-time monitoring. Advances in computer vision and deep learning have enabled facial recognition–based systems, but existing solutions face challenges with varying lighting, occlusions, camera angles, and limited parent communication.

This paper proposes Virtual Logger, a real-time, touchless attendance system that integrates YOLOv8 for face detection and FaceNet for face recognition. The system uses a Flask-based backend, SQLite database, and a React frontend for live monitoring, analytics, and secure data management. A unique feature is the automated parent notification module, which instantly informs parents about students’ attendance status.

The system is proxy-proof, accurate, scalable, and hygienic, ensuring real-time attendance logging, preventing duplicate entries, and providing transparent communication between institutions and parents. Its modular architecture combines live video capture, deep learning processing, backend logic, database management, and frontend visualization for seamless attendance management.

Conclusion

In this paper, a smart attendance management system called Virtual Logger was proposed to automate the traditional attendance process using face recognition technology. The system integrates YOLOv8 for real-time face detection and FaceNet for accurate face recognition, enabling reliable identification of registered users through live webcam input. The developed system successfully records attendance automatically, prevents duplicate entries, and stores attendance data securely in a centralized database. Additionally, the system provides an administrative dashboard for monitoring attendance statistics and includes an automated parent notification module to improve communication between institutions and guardians. Experimental results demonstrate that the proposed system achieves high detection and recognition accuracy while maintaining fast response time and efficient real-time performance. Compared with traditional manual or biometric attendance systems, the proposed approach offers a contactless, secure, and scalable solution for modern educational environments.

References

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Copyright

Copyright © 2026 M. Pallavi, B. Bhanusri, K. Tarangini, K. Girish Kumar, S. Bashira. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.