Deep Transfer Learning Approach for Student Attendance System During the COVID-19 Pandemic

Abstract

Marking students' attendance has been a challenge during the COVID-19 pandemic. It is a time-consuming task due to the abnormally high number of students present during a learning session; many studies have been proposed to improve the system. However, there are still issues with each of these systems; we have employed deep transfer learning techniques using six pretrained convolutional neural networks. We created a dataset of faces with masks and we used this dataset to assess six Convolutional Neural Network (CNN) models. We increased the training samples to improve the performance of the pre-trained models. The latter allows us to build a masked face recognition model of learners during a learning session. Due to the COVID-19 pandemic, students don facemasks to safeguard their own well-being and mitigate the spread of the virus. This has created a problem that did not exist before. The experimental findings reveal that pre-trained models, specifically caption and InceptionResNetV2, exhibit outstanding proficiency in precisely identifying faces with masks and require minimal training time.

Monitoring Student Attendance Through Vision Transformer-based Iris Recognition

“…Traditional attendance tracking solutions often rely on manual methods (roll calls, sign-in sheets), card-based systems (RFID [3] [4]), or biometric technologies [5] [6]. While these methods offer varying degrees of utility, they frequently encounter limitations in efficiency, security, and accuracy.…”