通过正交约束提升卷积核多样性，显著改善在线学习中的表情识别准确率。
OCFER-Net: Recognizing Facial Expression in Online Learning System
- 引入正则化强制卷积核正交，增强特征表达能力。
- 在FER-2013数据集上比基线模型高出1.087%准确率。
- 适合关注在线教育情感计算的研究者与开发者。
近年来，在全球新冠疫情背景下，在线学习愈发普及。除了知识传递，情绪互动同样重要，可通过面部表情识别（FER）实现。现有研究中，极少关注卷积核的正交性对特征提取的影响。本文提出OCFER-Net，通过正则化强制卷积核正交，以提取更具多样性和表现力的特征。在具有挑战性的FER-2013数据集上进行实验，结果表明该方法在准确率上优于多个基线模型，提升达1.087个百分点。相关代码已开源，地址为https://github.com/YeeHoran/OCFERNet。
原文摘要 · Abstract (English)
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.
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