Smart Attendance System Using Facial Recognition

Mridul ParadkarStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaSunaina SahuStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaHarshal SalekarStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaDiya SinghStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaProf. Manisha HatkarProfessor, Dept. of CSE (AI & ML), Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India

Vol 10 No 4 (2026): Volume 10, Issue 4, April 2026 | Pages: 153-159

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 15-04-2026

doi Logo doi.org/10.47001/IRJIET/2026.104022

Abstract

Smart Attendance System is an AI-based solution that automates attendance marking using face recognition technology. The system captures real-time video, detects faces, and marks attendance accurately without manual intervention. It integrates computer vision, machine learning, and a Flask-based web interface for efficient management. The system reduces proxy attendance and eliminates manual data entry errors. It uses Python, OpenCV, and the face recognition library to encode and match student faces against a stored dataset. The timetable is automatically identified using OpenPyXL to map the correct subject. This cost-effective, scalable solution is well-suited for modern educational institutions seeking to modernize their attendance workflows.

Keywords

Face Recognition, Attendance System, Computer Vision, Machine Learning, OpenCV, Flask, SQLite, Python


Citation of this Article

Mridul Paradkar, Sunaina Sahu, Harshal Salekar, Diya Singh, Prof. Manisha Hatkar. (2026). Smart Attendance System Using Facial Recognition. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(4), 153-159. Article DOI https://doi.org/10.47001/IRJIET/2026.104022

References
  1. OpenCV Development Team, OpenCV Documentation: Open Source Computer Vision Library, Available: https://docs.opencv.org
  2. A.Geitgey, face recognition: The World’s Simplest Facial Recognition API, Available: https://github.com/ageitgey/face_recognition
  3. D. E. King, “Dlib-ml: A Machine Learning Toolkit,” Journal of Machine Learning Research, vol. 10, pp.1755–1758, 2009.
  4. M. Turk and A. Pentland, “Eigen faces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
  5. P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001.
  6. Flask Development Team, Flask Web Framework Documentation, Available: https://flask.palletsprojects.com
  7. F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition And Clustering,” IEEE CVPR, 2015.