Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition
This addresses exam proctoring challenges for educators and institutions using video conferencing platforms, but it is incremental as it builds on existing face detection and recognition techniques.
The paper tackles the problem of ensuring integrity in online exams by presenting iExam, a system that combines lightweight face detection and deep recognition to monitor student presence and detect abnormal behaviors, achieving 90.4% accuracy in real-time detection and 98.4% in post-exam recognition.
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.