Abstract
Teacher expression recognition based on deep learning is an important application of deep learning in the field of education, which can quickly and accurately obtain teacher expressions, and save time and resources comparing with traditional manual classroom evaluation. In this paper, the GoogleNet-InceptionV3 convolutional neural network (CNN) model was proposed for teacher facial expression recognition. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used for CK+ dataset image enhancement. After training, a classification accuracy rate of 81.4% was achieved. Furthermore, we selected a teachers’ lecture video from MOOC website and analyzed it using the trained model. The correct recognition rate of the teachers’ facial expressions in this video is 90%. Teacher facial expression recognition technology based on deep learning provides a new idea and scheme for contemporary classroom teaching management and quality assessment.
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Acknowledgements
This work was supported by the Tianjin Science and Technology Planning Project under Grant No. 20JCYBJC00300, and the National Natural Science Foundation of China under Grant No. 11404240, and the Tianjin Philosophy and Social Science Planning Project under Grant No. TJJX17-016.
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Tian, Y., Han, T., Wu, L. (2021). Teacher Facial Expression Recognition Based on GoogLeNet-InceptionV3 CNN Model. In: Wang, W., Wang, G., Ding, X., Zhang, B. (eds) Artificial Intelligence in Education and Teaching Assessment. Springer, Singapore. https://doi.org/10.1007/978-981-16-6502-8_8
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DOI: https://doi.org/10.1007/978-981-16-6502-8_8
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