Online exams via video conference software like Zoom have been adopted in many schools due to COVID-19. While it is convenient, it is challenging for teachers to supervise online exams from simultaneously displayed student Zoom windows. In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis. To build such a novel system in its first kind, we overcome three challenges. First, we discover a lightweight approach to capturing exam video streams and analyzing them in real time. Second, we utilize the left-corner names that are displayed on each student's Zoom window and propose an improved OCR (optical character recognition) technique to automatically gather the ground truth for the student faces with dynamic positions. Third, we perform several experimental comparisons and optimizations to efficiently shorten the training and testing time required on teachers' PC. Our evaluation shows that iExam achieves high accuracy, 90.4% for real-time face detection and 98.4% for post-exam face recognition, while maintaining acceptable runtime performance. We have made iExam's source code available at https://github.com/VPRLab/iExam.
翻译:由于COVID-19,许多学校都通过视频会议软件(如Zom(Zom)等在线考试。虽然这是很方便的,但教师们很难从同时展示的学生Zom 窗口监督在线考试。在本文中,我们提议iExam,这是一个智能的在线考试监测和分析系统,这是一个智能的在线考试监测和分析系统,它不仅能够使用面对面检测来协助实时学生身份识别的巡视员,而且能够通过基于脸部识别的后ex-exam视频分析,发现常见的异常行为(包括面部消失、面部旋转脸和在考试期间由不同的人取代),因为COVID-19(COVID-19)。要建立这种新型的系统,我们克服了三个挑战。首先,我们发现了一种轻量的办法来实时捕捉考试视频流并分析这些视频流。第二,我们使用每个学生屏幕窗口上显示的左角点名字,不仅可以实时识别,而且可以提出改进的OCR(光性识别)技术来自动收集学生脸部的地面真相。第三,我们进行了几次实验性比较和优化的比较和优化,以便有效地缩短教师PC所需要的培训和测试时间。我们的评价显示iEx4在进行高端的检测时,我们进行了可接受的确认。