Facial Recognition for Student Attendance in Pandemic Contexts: A Deep Transfer Learning Approach

Zahra Al-Mutairi; Prof. Khalid A. Al-Qahtani

Authors

Zahra Al-Mutairi College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Prof. Khalid A. Al-Qahtani College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Keywords:

Facial recognition, student attendance monitoring, deep transfer learning, pandemic contexts

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.

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