Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation - Weiqing Wang; Kunliang Xu; Hongli Niu; Xiangrong Miao (Complexity)
Online education has developed rapidly due to its irreplaceable convenience. Under the severe circumstances caused by COVID-19 recently, many schools around the world have delayed opening and adopted online education as one of the main teaching methods. However, the efficiency of online classes has long been questioned. Compared with traditional face-to-face classes, there is a lack of direct, timely, and effective communication and feedback between teachers and students in the online courses. Previous studies have shown that there is a close and stable relationship between a person’s facial expressions and emotions generally. From the perspective of computer simulation, a framework combining a face expression recognition (FER) algorithm with online courses platforms is proposed in this work. The cameras in the devices are used to collect students’ face images, and the facial expressions are analyzed and classified into 8 kinds of emotions by the FER algorithm. An online course containing 27 students conducted on Tencent Meeting is used to test the proposed method, and the result proved that this method performs robustly in different environments. This framework can also be applied to other similar scenarios such as online meetings.