Abstract
It is very useful for the E-learning systems to detect the students emotional state accurately, and this can remind the teacher in time to change the teaching rhythm or content to meet the student’s emotional changes for making the teaching effect optimization. In this paper, we propose an emotion detection method based on a deep learning approach, Expectation-maximization Deep Spatial-Temporal Inference Network (EM-DeSTIN). This method takes the student’s facial expression as input and combine with Support Vector Machine (SVM) to implement emotion classification and identification. Experimental results show that the proposed method improves the performance of detecting emotion in a noisy environment compared with other methods.
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Notes
- 1.
How many centroids in a node depends on a balance between resource limitation and representational capacity.
- 2.
In DeSTIN, the belief state of a higher level node is called advice, which is the index of the winning centroid in the higher level node.
- 3.
Depending on various applications, we can take the belief values that come from different numbers of levels as a “feature”.
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- 5.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61003014 and No. 61673328), the National Social Science Foundation (15BYY082) and the Natural Science Foundation of Fujian Province of China (No. 2017J01128).
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Xu, J., Huang, Z., Shi, M., Jiang, M. (2018). Emotion Detection in E-learning Using Expectation-Maximization Deep Spatial-Temporal Inference Network. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_21
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