MMAP: A Multi-Modal Automated Online Proctor

被引：2

作者：

Gadekar, Aumkar ^{[1
]}

Oak, Shreya ^{[1
]}

Revadekar, Abhishek ^{[1
]}

Nimkar, Anant V. ^{[1
]}

机构：

[1] Sardar Patel Inst Technol, Dept Comp Engn, Mumbai, Maharashtra, India

来源：

MACHINE LEARNING AND BIG DATA ANALYTICS (PROCEEDINGS OF INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND BIG DATA ANALYTICS (ICMLBDA) 2021) | 2022年 / 256卷

关键词：

Online education; Automated proctor; Image processing; Authentication; Object detection; Eye tracking; Computer vision;

D O I：

10.1007/978-3-030-82469-3_28

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

With the surge in online education, more universities have shifted classes online. The growing popularity of MOOC courses and the changing education landscape could mean more and more people switching to online education. A primary drawback is the difficulty in monitoring of students during an online examination which leads to a lot of malpractices used by candidates. This paper explores computer vision based techniques to propose a five-fold proctoring mechanism for online tests. The features incorporated are authentication, head movement, eye motion tracking, speech detection and object detection. The solution has an overall accuracy of 91% accuracy.

引用

页码：314 / 325

页数：12

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