Paper
Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition
Authors
Xu Yang, Juantao Zhong, Daoyuan Wu, Xiao Yi, Jimmy H. M. Lee, Tan Lee, Peng Han
Abstract
Online exams, conducted via video conferencing platforms such as Zoom, have become popular in educational institutions since COVID-19. While convenient, ensuring the integrity and security of online exams remains challenging, as traditional invigilation struggles to effectively monitor multiple student video feeds in real time. In this paper, we present iExam, an effective online exam proctoring and analysis system that combines lightweight face detection and deep recognition. iExam employs real-time face detection to assist invigilators in continuously monitoring student presence, and leverages deep face recognition for post-exam video analysis to identify abnormal behaviors--including face disappearance, face rotation, and identity substitution. To realize this system, we address three core challenges: (i) designing a lightweight approach to efficiently capture and analyze exam video streams in real time; (ii) developing an enhanced OCR method to automatically extract student identities from dynamically positioned Zoom name tags, enabling reliable ground truth labeling without manual intervention; and (iii) optimizing the training and inference pipeline to significantlyreduce resource and time requirements on ordinary teacher devices. Extensive experiments demonstrate that iExam achieves 90.4% accuracy for real-time face detection and 98.4% accuracy for post-exam face recognition, while maintaining low overhead. These results show that iExam can substantially enhance the automation and reliability of online exam proctoring in practice.