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.

翻译：在当代背景下，信息技术的融合与人工智能的快速发展推动学校教育进入数字化与智能化的转型阶段。与此同时，由Covid-19疫情引发的全球范式转变加速了在线学习的演进，凸显了其重要性。在此发展进程中，在线教育模式中一个值得关注的关键方面是数字学习领域中的身份认证。基于此背景，本研究深入探讨了一种在线学习身份认证解决方案，采用改进的卷积神经网络架构，特别是残差网络模型。通过利用深度学习技术，该方法旨在推动在线教育的持续发展，同时增强其安全性与稳定性。这种强化对于使在线教育能够无缝适应教育格局的快速演进至关重要。本文的核心方案涉及部署YOLOv5网络，该网络基于我们自主构建的数据集进行精细训练。该网络负责从学生开启的在线摄像头捕获的图像中识别出人脸。随后，将获得的人脸信息输入残差网络以提取更深层次的精细特征。最后，通过与学生人脸数据库进行欧氏距离的对比分析，有效确认每位学生的身份。