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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.13356 |
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| _version_ | 1866918265691832320 |
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| author | Yang, Xu Zhong, Juantao Wu, Daoyuan Yi, Xiao Lee, Jimmy H. M. Lee, Tan Han, Peng |
| author_facet | Yang, Xu Zhong, Juantao Wu, Daoyuan Yi, Xiao Lee, Jimmy H. M. Lee, Tan Han, Peng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_13356 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition Yang, Xu Zhong, Juantao Wu, Daoyuan Yi, Xiao Lee, Jimmy H. M. Lee, Tan Han, Peng Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2206.13356 |