Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{170099,
        author = {Pandla Rohith and C.Surekha and M.Shirisha and Deepika Singh},
        title = {Enhanced Attendance System Based On Real Time Face Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3391-3395},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170099},
        abstract = {We propose an advanced attendance system combining facial recognition and eye-blink detection for enhanced security and accuracy. Using the ESP32-CAM module, OpenCV library, and Visual Studio IDE, the system captures and analyzes facial images. Unlike traditional systems, our dual-layer approach includes eye-blink detection to prevent spoofing and false positives, ensuring only active and present individuals are marked for attendance. This solution addresses vulnerabilities in existing methods, offering a reliable, secure, and cost-effective approach suitable for educational and professional settings where active engagement is essential.},
        keywords = {Facial recognition, Automated attendance system, Deep learning, Arduino, Educational technology.},
        month = {November},
        }