Open Access
Facial Recognition for Student Attendance in Pandemic Contexts: A Deep Transfer Learning Approach
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College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Abstract
The COVID-19 pandemic introduced unprecedented challenges to traditional student attendance systems, necessitating contactless and mask-compliant solutions. This article presents a novel approach for student attendance leveraging deep transfer learning for robust facial recognition, even in the presence of face masks. Traditional attendance methods, often manual or touch-based, posed health risks and logistical difficulties during the pandemic. Our proposed system integrates pre-trained deep convolutional neural networks (CNNs) for efficient feature extraction and classification, significantly reducing the need for extensive training data and computational resources. By fine-tuning these models on datasets of masked and unmasked faces, the system achieves high accuracy in identifying students under varying conditions, including partial facial occlusion. Experimental results demonstrate the efficacy and efficiency of the transfer learning paradigm, showcasing its potential to provide a reliable, automated, and safe attendance solution for educational institutions in a post-pandemic or similar health crisis environment.
How to Cite
Zahra Al-Mutairi, & Prof. Khalid A. Al-Qahtani. (2025). Facial Recognition for Student Attendance in Pandemic Contexts: A Deep Transfer Learning Approach. Frontiers in Emerging Computer Science and Information Technology, 2(04), 10–12. Retrieved from https://irjernet.com/index.php/fecsit/article/view/101
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