Real-time Facial Expression Recognition via Dense & Squeeze-and-Excitation Blocks

Due to the coronavirus disease 2019 (COVID-19) pandemic, traditional face-to-face courses have been transformed into online and e-learning courses. Although online courses provide flexible teaching and learning in terms of time and place, teachers cannot be fully aware of their students’ individual learning situation and emotional state. The cognition of learning emotion with facial expression recognition has been a vital issue in recent years. To achieve affective computing, the paper presented a fast recognition model for learning emotions through Dense Squeeze-and-Excitation Networks (DSENet), which rapidly recognizes students’ learning emotions, while the proposed real-time online feedback system notifies teacher instantaneously. Firstly, DSENet is trained and validated by an open dataset called Facial Expression Recognition 2013. Then, we collect students’ learning emotions from e-learning classes and apply transfer learning and data augmentation techniques to improve the testing accuracy.