Computer Vision based Online Job Interview Proctoring for Campus Placement

This paper presents an innovative solution for enhancing the integrity, fairness, and efficiency of online job interviews conducted for campus placements. The proposed system utilizes computer vision techniques to proctor interviews, providing real-time monitoring and analysis of candidate behaviors. By leveraging this technology, companies can reduce costs associated with placement drives, ensure fairness for all candidates, and empower human proctors with valuable insights. This paper highlights the benefits of computer vision-based online job interview proctoring, referencing relevant studies and industry practices. A yolo model is fine tuned for custom object detection and it is used in the proposed algorithm. The algorithm detects the candidate’s malpractices in online interviews with fewer false positives.