# OCFER-Net: Recognizing Facial Expression in Online Learning System Source: [OCFER-Net: Recognizing Facial Expression in Online Learning System]() Collection index: [llms.txt]() Data updated: 2025-12-10T05:04:31.849Z Authors: Yi Huo, Lei Zhang Published: 2025-12-06T10:19:48.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Facial Expression Recognition on FER Private 2013 (test)](): Facial Expression Recognition; FER Private 2013 (test); Accuracy: 73.475. - [Facial Expression Recognition on FER Public 2013 (test)](): Facial Expression Recognition; FER Public 2013 (test); Accuracy: 71.218. ## Original sources and implementation [Original paper]() [PDF]()