Facial Recognition Attendance System with Integrated Liveness Detection and Real-Time Reporting

Abstract

Facial recognition technology can be an efficient and touch-free biometric technology for automating attendance in academic and organizational environments. The paper presents a comprehensive Facial Recognition Attendance System with Integrated Liveness Detection and Real-Time Reporting. The system is developed using Python and OpenCV library. The system utilizes a webcam for facial images, Haar Cascade for face detection, and LBPH for face recognition. The attendance data is stored in SQLite and displayed using a Flask-based web interface. The liveness detection component eliminates spoofing attacks by images or videos by detecting eye blinks and movements through frame differencing and motion analysis. The system follows a modular approach for data collection, model training, real-time face recognition along with liveness detection, and automatic attendance recording. Experiments were conducted on a wide range of users in different poses and lighting conditions. The facial recognition accuracy was 96.3%, liveness detection precision was 94.5%, and average face recognition time was 1.28 seconds. The system can run on normal desktop/laptop computers without a GPU and can be suitable for small and medium-scale organizations. The facial recognition-based attendance system is a cost- effective and efficient solution that can replace existing manual and RFID-based solutions. The system can record secure and touch-free automatic attendance data in real-time. The system can be further extended to use deep learning techniques and can be integrated with other tools and technologies.

Introduction

Traditional attendance systems—manual registers, RFID, QR codes, or contact-based biometrics—are either time-consuming, error-prone, or raise hygiene concerns. Manual and semi-automated methods are vulnerable to proxy attendance, lost or shared tokens, and lack real-time analytics. Contactless solutions, especially facial recognition, offer convenience and hygiene but are susceptible to spoofing without liveness detection.

The proposed system is a Facial Recognition Attendance System with Integrated Liveness Detection and Real-Time Reporting, implemented using Python, OpenCV, Flask, and SQLite. It uses webcams for face capture, Haar Cascade for detection, LBPH for recognition, and motion/eye-blink analysis for liveness verification. Attendance records are stored in SQLite and displayed via a Flask web interface, enabling real-time monitoring, reporting, and export. The system is lightweight, contactless, easy to deploy on standard hardware, and designed to scale for classrooms or office environments.

Conclusion

Facial Recognition Attendance System with Liveness De- tection and Real-Time Reporting provides a safe and efficient means of attendance tracking. It uses a facial recognition system with liveness detection, ensuring minimal errors and proxy attendance, and provides a seamless experience for users. Results of experiments on the system indicate high recog- nition accuracy, effective spoof detection, and fast response times, making it a promising system for attendance tracking. Possible enhancements could include deep learning algo- rithms like FaceNet and ArcFace, liveness detection with texture and depth information, cloud deployment for a multi- site system, and a mobile app for remote attendance tracking and alerts.

References

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Copyright

Copyright © 2026 Ms. Neelam, N. Sowjanya, K. Sharvani, V. Varshini. 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.