Automated Spatio-Temporal Attendance Verification: A Computer Vision  Framework Utilizing Convolutional Neural Networks

Suram Supraja , Dr. G.V. Ramesh Babu

Automated Spatio-Temporal Attendance Verification: A Computer Vision Framework Utilizing Convolutional Neural Networks

Authors

Suram Supraja , Dr. G.V. Ramesh Babu

Keywords:

Face Recognition, Smart Attendance System, Histogram of Oriented Gradients (HOG), Convolutional Neural Networks (CNN), OpenCV

Abstract

Attendance management is an essential process in educational institutions and organizations formonitoring the presence and participation of students and employees. Traditional attendance recordingmethods, such as manual registers and identification cards, are often time-consuming, error-prone, and susceptible to proxy attendance. To overcome these limitations

References

Vegro, C.L.R.; de Almeida, L.F. Global coffee market: Socio-economic and cultural dynamics. In Coffee Consumption and Industry Strategies

in Brazil; Woodhead Publishing: Cambridge, UK, 2020; pp. 3–19. [Google Scholar]

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Published

2026-06-14

How to Cite

Suram Supraja , Dr. G.V. Ramesh Babu. (2026). Automated Spatio-Temporal Attendance Verification: A Computer Vision Framework Utilizing Convolutional Neural Networks . Journal of Computational Analysis and Applications (JoCAAA), 35(6), 67–75. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5573