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Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation
Complexity ( IF 2.3 ) Pub Date : 2020-09-11 , DOI: 10.1155/2020/4065207
Weiqing Wang 1 , Kunliang Xu 1 , Hongli Niu 1 , Xiangrong Miao 2
Affiliation  

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.

中文翻译:

基于计算机仿真的在线教育中基于面部表情的学生情感识别

在线教育由于无可替代的便利性而迅速发展。在最近由COVID-19引起的严峻环境下,世界上许多学校都推迟了开放时间,并将在线教育作为主要教学方法之一。但是,在线课程的效率一直受到质疑。与传统的面对面课程相比,在线课程中师生之间缺乏直接,及时,有效的沟通和反馈。先前的研究表明,一个人的面部表情与情绪之间通常存在着紧密而稳定的关系。从计算机仿真的角度出发,提出了一种结合面部表情识别(FER)算法和在线课程平台的框架。设备中的摄像头用于收集学生的面部图像,并通过FER算法将面部表情分析并分为8种情绪。该方法在腾讯会议上在线包含27名学生,用于测试该方法,结果证明该方法在不同环境下均具有良好的性能。该框架还可以应用于其他类似方案,例如在线会议。
更新日期:2020-09-11
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