  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.
