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Emotion Detection in E-learning Using Expectation-Maximization Deep Spatial-Temporal Inference Network

  • Jiangqin Xu
  • Zhongqiang Huang
  • Minghui Shi
  • Min JiangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

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.

Keywords

E-learning Emotion detection Deep learning 

Notes

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jiangqin Xu
    • 1
  • Zhongqiang Huang
    • 2
  • Minghui Shi
    • 2
  • Min Jiang
    • 2
    Email author
  1. 1.College of Foreign Languages and CulturesXiamen UniversityXiamenChina
  2. 2.Fujian Province Key Laboratory for Brain-inspired Computing Technique and Applications, School of Information and EngineeringXiamen UniversityXiamenChina

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