Online exams conducted via video conferencing platforms such as Zoom have become widespread, yet ensuring exam integrity remains challenging due to the difficulty of monitoring multiple video feeds in real time. We present iExam, an online exam proctoring and analysis system that combines lightweight real-time face detection with deep face recognition for postexam analysis. iExam assists invigilators by monitoring student presence during exams and identifies abnormal behaviors, such as face disappearance, face rotation, and identity substitution, from recorded videos. The system addresses three key challenges: (i)efficient real-time video capture and analysis, (ii) automated student identity labeling using enhanced OCR on dynamic Zoom name tags, and (iii) resource-efficient training and inference on standard teacher devices. Extensive experiments show that iExam achieves 90.4% accuracy in real-time face detection and 98.4% accuracy in post-exam recognition with low overhead, demonstrating its practicality and effectiveness for online exam proctoring.

翻译：通过Zoom等视频会议平台进行的在线考试已广泛普及，但由于实时监控多个视频流的困难，确保考试诚信仍具挑战性。本文提出iExam——一种结合轻量级实时人脸检测与考后深度人脸识别的在线考试监考分析系统。iExam通过监控考试期间学生的在位状态，并从录制视频中识别人脸消失、面部转动及身份替换等异常行为，为监考人员提供辅助。该系统解决了三个关键挑战：(i)高效的实时视频捕获与分析，(ii)通过增强的动态Zoom姓名标签OCR技术实现自动化学生身份标注，以及(iii)在标准教师设备上进行资源高效的训练与推理。大量实验表明，iExam在实时人脸检测中达到90.4%的准确率，在考后识别中达到98.4%的准确率且系统开销较低，证明了其在实际在线考试监考场景中的实用性与有效性。