Abstract: The explicit facial expressions of learners provide a crucial measure for exploring their implicit epistemic emotions. A successfully accurate recognition of the epistemic emotion facial expressions in a real e-learning environment is still challenging due to its low change in intensity and short duration. In this paper， a new dual-modality spatiotemporal feature representation learning for recognizing facial expression in e-learning is proposed. Spatiotemporal geometrical feature representations and spatial-temporal appearance feature representations of facial expressions are designed to be automatically extracted with a hybrid deep neural network. The dual-modality feature fusion representations are used to recognize facial expressions. First， the experiment of micro-expression recognition is conducted on a spontaneous micro-expression dataset. The experimental result shows that the proposed method achieves higher recognition accuracy compared to the state-of-the-art methods. Next， a dataset of facial expression of epistemic emotions is created. Then， the recognition experiment of facial expression of epistemic emotions is conducted， and the model of micro-expression recognition is used in the model training of facial expression recognition of epistemic emotions by transfer learning. The multiple metrics are adopted to evaluate the model of facial expression recognition of epistemic emotions， and the experimental results demonstrate that the model is robust and efficient for the facial expression recognition of epistemic emotions.

Key words: Key words: facial expression recognition, epistemic emotion, artificial intelligence, deep neural network, e-learning