Mask-Robust Face Verification for Online Learning via YOLOv5 and Residual Networks

TL;DR

This paper presents a face verification system for online learning that combines YOLOv5 for face detection and residual networks for feature extraction, enhancing security and accuracy in digital student authentication.

Contribution

It introduces a novel integration of YOLOv5 and residual networks specifically tailored for online student identity verification, trained on a proprietary dataset.

Findings

High accuracy in face verification from online camera images

Effective identification of students in real-time online environments

Enhanced security for online education platforms

Abstract

In the contemporary landscape, the fusion of information technology and the rapid advancement of artificial intelligence have ushered school education into a transformative phase characterized by digitization and heightened intelligence. Concurrently, the global paradigm shift caused by the Covid-19 pandemic has catalyzed the evolution of e-learning, accentuating its significance. Amidst these developments, one pivotal facet of the online education paradigm that warrants attention is the authentication of identities within the digital learning sphere. Within this context, our study delves into a solution for online learning authentication, utilizing an enhanced convolutional neural network architecture, specifically the residual network model. By harnessing the power of deep learning, this technological approach aims to galvanize the ongoing progress of online education, while concurrently bolstering its security and stability. Such fortification is imperative in enabling online education to seamlessly align with the swift evolution of the educational landscape. This paper's focal proposition involves the deployment of the YOLOv5 network, meticulously trained on our proprietary dataset. This network is tasked with identifying individuals' faces culled from images captured by students' open online cameras. The resultant facial information is then channeled into the residual network to extract intricate features at a deeper level. Subsequently, a comparative analysis of Euclidean distances against students' face databases is performed, effectively ascertaining the identity of each student.