A Facial Recognition Based-Attendance Monitoring System for Jesus Reigns Christian College Staff

Artificial Intelligence (AI) has become one of the most significant technological advancements in modern society, improving automation, security, and operational efficiency across different industries. In educational institutions, attendance monitoring remains an essential administrative task; however, traditional attendance systems are often time-consuming, prone to human error, and vulnerable to attendance fraud. Facial recognition technology offers a modern solution by enabling automatic identification and verification of individuals through digital image processing. This study presents AttendScan, a Facial Recognition-Based Attendance Monitoring System developed for the staff of Jesus Reigns Christian College. The main objective of the system is to automate attendance recording and provide a centralized platform for monitoring and managing staff attendance records. The system utilizes facial recognition technology for real-time identification and verification of staff members during time-in and time-out procedures.AttendScan was developed using the Agile Software Development Life Cycle methodology. The system uses Python and Flask for backend development, HTML, CSS, and JavaScript for the web interface, OpenCV for facial detection and recognition, and MySQL for database management. The system includes features such as staff registration, facial data capture, attendance logging, and report generation.The implementation of AttendScan provides a more efficient, accurate, and secure attendance monitoring process compared to manual attendance methods. The system minimizes administrative workload, reduces attendance manipulation, and improves record accessibility through digital automation. The study concludes that facial recognition technology can significantly enhance attendance management in educational institutions by providing reliable and automated monitoring solutions.

Facial Recognition, Attendance Monitoring System, Artificial Intelligence, OpenCV

1. Barhate, M., Ramdasi, I., Shaikh, I., Ingawale, V., Ingle, P., Ingle, P., & Jadhao, M. (2024). Innovative attendance tracking: Facial recognition. 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), 1–6. https://doi.org/10.1109/esci59607.2024.10497364 [Google Scholar] [Crossref]

2. Bavaskar, V. (2024). Face recognition attendance system (Undergraduate dissertation, Sant Gadge Baba AmravatiUniversity,Amravati). https://www.ssgmce.ac.in/uploads/UG_Projects/cse/202324/Project%20Report%20Gr.%20No.%2010_2023-24.pdf [Google Scholar] [Crossref]

3. Bondugula, V., Thummanapelli, S., Yadagiri, S., & Kumari, A. (2025). Advancements in student attendance management: A facial recognition-based system. AIP Conference Proceedings, 3297(1), 060012. AIP Publishing. https://doi.org/10.1063/5.0294678 [Google Scholar] [Crossref]

4. Chempavathy, B., Dhanalakshmi, M., Varun, M. G., Bohra, V., & Kalsariya, V. A. (2024). An improved attendance monitoring system through facial recognition using RetinaFace algorithm. 2024 3rd International Conference for Innovation in Technology (INOCON), 1–5. https://doi.org/10.1109/inocon60754.2024.10511664 [Google Scholar] [Crossref]

5. Chukwude, O. M., Surajudeen-Bakinde, N. T., Zakariyya, S. O., Ogunsakin, J. B., Akanni, J., Olayanju, S. A., & Ehiagwina, F. O. (2023). Development of a face recognition-based attendance system. SLU Journal of Science and Technology,6,214–224. https://api-uilspace.unilorin.edu.ng/server/api/core/bitstreams/ad26d58c-9829-4597-b0e1-e8a07383a8e6/content [Google Scholar] [Crossref]

6. Darshan, V., Kenny, D. S., Shreyas, H. R., Vinay, N., & Revathi, S. (2024). IoT-based attendance monitoring system using facial recognition. 2024 Second International Conference on Advances in Information Technology (ICAIT), 1–6. https://doi.org/10.1109/icait61638.2024.10690462 [Google Scholar] [Crossref]

7. Karthick, V., Kumar, P., Divyaprasath, R., & Kesarinath, K. (2024). Facial recognition based attendance management system. SSRN. https://doi.org/10.2139/ssrn.4834649 [Google Scholar] [Crossref]

8. Kumar, N. (2025). Smart attendance by using face recognition. 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 1–5. https://doi.org/10.1109/IITCEE64140.2025.10915432 [Google Scholar] [Crossref]

9. Kumar, V., Yadav, A., Rai, A., Narwaria, M., & Mishra, R. (2025). Facial recognition for attendance systems: Trends and challenges. In the International Conference on Mathematics and Logics in Computer Science (pp. 402–411). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-14156-9_33 [Google Scholar] [Crossref]

10. Kumar, V. N., & Kumar, S. (2024). Real time attendance system using facial recognition. In Multidisciplinary Approaches for Sustainable Development (pp. 317–320). CRC Press. https://doi.org/10.1201/9781003543633-51 [Google Scholar] [Crossref]

11. Patel, K., Zagade, A., & Gupta, D. (2024). Automated facial authentication attendance system. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 12(4). https://doi.org/10.22214/ijraset.2024.59809 [Google Scholar] [Crossref]

12. Raj, A., Srivastav, H., Shukla, S., & Gupta, N. (2024). Facial recognition-based student attendance system. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–5. https://doi.org/10.1109/ICRITO61523.2024.10522165 [Google Scholar] [Crossref]

13. Sain, A., Dutta, S., Saha, R., & Mandal, U. (2023). Automated facial recognition-based attendance system using OpenCV in Python. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(6), 105–111. https://doi.org/10.32628/CSEIT2390617 [Google Scholar] [Crossref]

14. Sharma, S. K., Kumar, S., Kumar, S., Prajapati, S., & Ansari, I. (2024). Smart attendance system using face recognition. 2024 International Conference on Computing, Sciences and Communications (ICCSC), 1–7. https://doi.org/10.1109/ICCSC62048.2024.10830336 [Google Scholar] [Crossref]

15. Vardhan, R., Aftab, S., Sanyal, N., Ali, K., & Kumar, A. (2025). Automated attendance system using facial recognition: A comprehensive review. In Challenges and Opportunities for Innovation in India (pp. 117–122). Taylor & Francis. https://doi.org/10.1201/9781003606260-20 [Google Scholar] [Crossref]

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