文献类型：期刊文献

英文题名：OCFER-Net: Recognizing Facial Expression in Online Learning System

作者：Huo, Yi[1]; Zhang, Lei[2]

第一作者：霍奕

机构：[1] AI& Edu Lab, Teacher’s College, Beijing Union University, Beijing, China; [2] College of Computer Science, Comm. University of China, Beijing, China

第一机构：北京联合大学师范学院

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20260012036)

语种：英文

外文关键词：COVID-19 - E-learning - Face recognition - Learning systems - Teaching

摘要：Recently, online learning is very popular, especially under the global epidemic of COVID-19. Besides knowledge distribution, emotion interaction is also very important. It can be obtained by employing Facial Expression Recognition (FER). Since the FER accuracy is substantial in assisting teachers to acquire the emotional situation, the project explores a series of FER methods and finds that few works engage in exploiting the orthogonality of convolutional matrix. Therefore, it enforces orthogonality on kernels by a regularizer, which extracts features with more diversity and expressiveness, and delivers OCFER-Net. Experiments are carried out on FER-2013, which is a challenging dataset. Results show superior performance over baselines by 1.087. The code of the research project is publicly available on https://github.com/YeeHoran/OCFERNet. Copyright ? 2025, The Authors. All rights reserved.