מיכאל פייר

Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants' facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.