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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

AN APPLICATION ON MACHINE LEARNING AND COMPUTER VISION FOR AUTOMATED ATTENDANCE TRACKING SOLUTION

Bala Krishna Chennaiah

Dr.Vanitha Kakollu

Department of Computer Science GSS GITAM Deemed to be University Visakhapatnam
DOI:https://doi.org/10.55041/ijcope.v2i4.260

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Abstract

Marking attendance might seem like the simplest task in a classroom, but anyone who has sat through a ten-minute roll call at the start of a lecture, or tried to maintain fair records while students mark each other present, knows how broken the process really is. This paper presents FaceTrack Pro, an automated attendance tracking system built using Machine Learning and Computer Vision that was designed to fix exactly these problems. The system uses RetinaFace for real-time face detection, ArcFace R100 for face recognition with a benchmark accuracy of 99.83% on the LFW dataset, and MediaPipe FaceMesh for liveness verification through eye-blink detection which means no one can fool the system by holding up a photograph. It runs entirely on a regular computer, needs no internet connection after the first setup, and stores all data as plain CSV files that any staff member can open in Excel without needing a database. When tested with forty students across different classroom environments, the system achieved 99.1% recognition accuracy, detected faces correctly in 97.2% of frames, and marked attendance in an average of 1.8 seconds per student. FaceTrack Pro is not just a research prototype  it is a working system that can be deployed in a real institution today.

Keywords:

Face Recognition, Automated Attendance, RetinaFace, ArcFace R100, MediaPipe FaceMesh, Liveness Detection, Eye Aspect Ratio, Computer Vision, Deep Learning, InsightFace, Python, Anti-Spoofing, Student Tracking

How to Cite this Paper

Chennaiah, B. K. (2026). An Application on Machine Learning and Computer Vision for Automated Attendance Tracking Solution. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.260

Chennaiah, Bala. "An Application on Machine Learning and Computer Vision for Automated Attendance Tracking Solution." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.260.

Chennaiah, Bala. "An Application on Machine Learning and Computer Vision for Automated Attendance Tracking Solution." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.260.

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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 11 2026
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