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{170311,
        author = {Vichithra V R and Jeba Priya P},
        title = {Attendance Management System Using Face Recognition from Group Photos Taken Through a Webcam Controlled by Raspberry Pi},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {87-93},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170311},
        abstract = {This paper presents a novel attendance management system that utilizes facial recognition technology to automatically mark attendance from group photos, implemented on a Raspberry Pi 3. The system captures images of students during classroom sessions, processes these photos to identify individuals, and logs their attendance based on facial recognition algorithms. By using the face_recognition library, which relies on deep learning for face detection and matching, the system accurately identifies students in group settings where multiple faces are present. The system operates on the Raspberry Pi 3, a low-cost and portable device, making it a feasible solution for educational institutions seeking an affordable and efficient attendance tracking method. The system's design ensures accurate performance under various classroom conditions, including different lighting and facial orientations. Attendance data is securely stored in a MySQL database, offering easy access and management for instructors. Privacy concerns are addressed by implementing encryption techniques to protect biometric data. Through this approach, the paper demonstrates the practical application of facial recognition in automating attendance management, highlighting the potential for cost-effective and scalable solutions in educational environments. The proposed system provides a reliable, easy-to-deploy alternative to traditional attendance methods while maintaining data security and user privacy.},
        keywords = {Facial Recognition, Raspberry Pi 3, Group Photos, Classroom Attendance, Machine Learning, Automation, Face Detection, Deep Learning},
        month = {December},
        }