초록 열기/닫기 버튼
Due to the spread of COVID-19, the online lecture has become more prevalent. However, it was found that a lot of students and professors are experiencing lack of communication. This study is therefore designed to improve interactive communication between professors and students in real-time online lectures. To do so, we explore deep learning approaches for automatic recognition of students’ facial expressions and classification of their understanding into 3 classes (Understand / Neutral / Not Understand). We use ‘BlazeFace’ model for face detection and ‘ResNet-GRU’ model for facial expression recognition (FER). We name this entire process ‘Degree of Understanding (DoU)’ algorithm. DoU algorithm can analyze a multitude of students collectively and present the result in visualized statistics. To our knowledge, this study has great significance in that this is the first study offers the statistics of understanding in lectures using FER. As a result, the algorithm achieved rapid speed of 0.098sec/frame with high accuracy of 94.3% in CPU environment, demonstrating the potential to be applied to real-time online lectures. DoU Algorithm can be extended to various fields where facial expressions play important roles in communications such as interactions with hearing impaired peopl
