  Due to the recent outbreak of COVID-19, many classes, exams, and meetings
have been conducted non-face-to-face. However, the foundation for video
conferencing solutions is still insufficient. So this technology has become an
important issue. In particular, these technologies are essential for
non-face-to-face testing, and technology dissemination is urgent. In this
paper, we present a single video conferencing solution using gaze estimation in
preparation for these problems. Gaze is an important cue for the tasks such as
analysis of human behavior. Hence, numerous studies have been proposed to solve
gaze estimation using deep learning, which is one of the most prominent methods
up to date. We use these gaze estimation methods to detect abnormal behavior of
video conferencing participants. Our contribution is as follows. i) We find and
apply the optimal network for the gaze estimation method and apply a
self-supervised method to improve accuracy. ii) For anomaly detection, we
present a new dataset that aggregates the values of a new gaze, head pose, etc.
iii) We train newly created data on Multi Layer Perceptron (MLP) models to
detect anomaly behavior based on deep learning. We demonstrate the robustness
of our method through experiments.
