Given that approximately half of science, technology, engineering, and
mathematics (STEM) undergraduate students in U.S. colleges and universities
leave by the end of the first year [15], it is crucial to improve the quality
of classroom environments. This study focuses on monitoring students' emotions
in the classroom as an indicator of their engagement and proposes an approach
to address this issue. The impact of different facial parts on the performance
of an emotional recognition model is evaluated through experimentation. To test
the proposed model under partial occlusion, an artificially occluded dataset is
introduced. The novelty of this work lies in the proposal of an occlusion-aware
architecture for facial action units (AUs) extraction, which employs attention
mechanism and adaptive feature learning. The AUs can be used later to classify
facial expressions in classroom settings.
This research paper's findings provide valuable insights into handling
occlusion in analyzing facial images for emotional engagement analysis. The
proposed experiments demonstrate the significance of considering occlusion and
enhancing the reliability of facial analysis models in classroom environments.
These findings can also be extended to other settings where occlusions are
prevalent.

通过监测学生在课堂中的情绪作为参与度的指标，并提出了一种方法来解决这个问题，本研究评估了不同面部部位对情绪识别模型性能的影响，并引入了一个人工遮挡数据集来测试部分遮挡下的提议模型。研究的创新之处在于提出了一种面部动作单元（AUs）提取的遮挡感知架构，该架构采用了注意机制和自适应特征学习，而这些 AUs 可以用于分类课堂环境中的面部表情。研究的发现为分析面部图像的情绪参与分析中处理遮挡提供了宝贵的见解，实验结果表明在课堂环境中考虑遮挡并提高面部分析模型的可靠性的重要性，并且这些结果也可以推广到其他普遍存在遮挡的场景。