The exploration of facial expression recognition in distance education learning system

Ai Sun, Yingjian Li, Yueh-Min Huang, Qiong Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, the learning style of modern distance education has been more and more popular among the learners. However, the learner’s emotion is often ignored during the distance education learning process. In this paper, the study purpose is mainly concerned with how to effectively recognize the emotion through the way of using facial expression for the future distance education learning. We apply the method of Convolutional Neural Network (CNN) in our research. First, we introduce the structure of CNN in terms of the convolutional layers, sub-sampling layers and fully connected layers. Secondly, we propose a framework to use CNN in distance education system. Thirdly, we carry out experiment on a data set that consists of facial expression images of learners to evaluate the performance of proposed method. Finally, we get the conclusion the average accuracy of CNN to recognize the facial expression is 93.63%. The high accuracy shows the application of CNN to recognize facial expression is valuable and helpful for the teachers in the future distance education system to attain and understand the learners’ emotion state in real time, accordingly to regulate the teaching strategy in time.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings
EditorsLin Lin, Ting-Ting Wu, Yueh-Min Huang, Yueh-Min Huang, Andreja Istenic Starcic, Rustam Shadieva
PublisherSpringer Verlag
Pages111-121
Number of pages11
ISBN (Print)9783319997360
DOIs
Publication statusPublished - 2018 Jan 1
Event1st International Conference on Innovative Technologies and Learning, ICITL 2018 - Portoroz, Slovenia
Duration: 2018 Aug 272018 Aug 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11003 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Innovative Technologies and Learning, ICITL 2018
CountrySlovenia
CityPortoroz
Period18-08-2718-08-30

Fingerprint

Distance Education
Facial Expression Recognition
Distance education
Learning Systems
Facial Expression
Learning systems
Neural Networks
Neural networks
Learning Styles
Subsampling
Learning Process
Teaching
High Accuracy
Sampling
Evaluate
Experiment
Emotion
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sun, A., Li, Y., Huang, Y-M., & Li, Q. (2018). The exploration of facial expression recognition in distance education learning system. In L. Lin, T-T. Wu, Y-M. Huang, Y-M. Huang, A. I. Starcic, & R. Shadieva (Eds.), Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings (pp. 111-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11003 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-99737-7_11
Sun, Ai ; Li, Yingjian ; Huang, Yueh-Min ; Li, Qiong. / The exploration of facial expression recognition in distance education learning system. Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings. editor / Lin Lin ; Ting-Ting Wu ; Yueh-Min Huang ; Yueh-Min Huang ; Andreja Istenic Starcic ; Rustam Shadieva. Springer Verlag, 2018. pp. 111-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "The exploration of facial expression recognition in distance education learning system",
abstract = "In recent years, the learning style of modern distance education has been more and more popular among the learners. However, the learner’s emotion is often ignored during the distance education learning process. In this paper, the study purpose is mainly concerned with how to effectively recognize the emotion through the way of using facial expression for the future distance education learning. We apply the method of Convolutional Neural Network (CNN) in our research. First, we introduce the structure of CNN in terms of the convolutional layers, sub-sampling layers and fully connected layers. Secondly, we propose a framework to use CNN in distance education system. Thirdly, we carry out experiment on a data set that consists of facial expression images of learners to evaluate the performance of proposed method. Finally, we get the conclusion the average accuracy of CNN to recognize the facial expression is 93.63{\%}. The high accuracy shows the application of CNN to recognize facial expression is valuable and helpful for the teachers in the future distance education system to attain and understand the learners’ emotion state in real time, accordingly to regulate the teaching strategy in time.",
author = "Ai Sun and Yingjian Li and Yueh-Min Huang and Qiong Li",
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Sun, A, Li, Y, Huang, Y-M & Li, Q 2018, The exploration of facial expression recognition in distance education learning system. in L Lin, T-T Wu, Y-M Huang, Y-M Huang, AI Starcic & R Shadieva (eds), Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11003 LNCS, Springer Verlag, pp. 111-121, 1st International Conference on Innovative Technologies and Learning, ICITL 2018, Portoroz, Slovenia, 18-08-27. https://doi.org/10.1007/978-3-319-99737-7_11

The exploration of facial expression recognition in distance education learning system. / Sun, Ai; Li, Yingjian; Huang, Yueh-Min; Li, Qiong.

Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings. ed. / Lin Lin; Ting-Ting Wu; Yueh-Min Huang; Yueh-Min Huang; Andreja Istenic Starcic; Rustam Shadieva. Springer Verlag, 2018. p. 111-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11003 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - The exploration of facial expression recognition in distance education learning system

AU - Sun, Ai

AU - Li, Yingjian

AU - Huang, Yueh-Min

AU - Li, Qiong

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N2 - In recent years, the learning style of modern distance education has been more and more popular among the learners. However, the learner’s emotion is often ignored during the distance education learning process. In this paper, the study purpose is mainly concerned with how to effectively recognize the emotion through the way of using facial expression for the future distance education learning. We apply the method of Convolutional Neural Network (CNN) in our research. First, we introduce the structure of CNN in terms of the convolutional layers, sub-sampling layers and fully connected layers. Secondly, we propose a framework to use CNN in distance education system. Thirdly, we carry out experiment on a data set that consists of facial expression images of learners to evaluate the performance of proposed method. Finally, we get the conclusion the average accuracy of CNN to recognize the facial expression is 93.63%. The high accuracy shows the application of CNN to recognize facial expression is valuable and helpful for the teachers in the future distance education system to attain and understand the learners’ emotion state in real time, accordingly to regulate the teaching strategy in time.

AB - In recent years, the learning style of modern distance education has been more and more popular among the learners. However, the learner’s emotion is often ignored during the distance education learning process. In this paper, the study purpose is mainly concerned with how to effectively recognize the emotion through the way of using facial expression for the future distance education learning. We apply the method of Convolutional Neural Network (CNN) in our research. First, we introduce the structure of CNN in terms of the convolutional layers, sub-sampling layers and fully connected layers. Secondly, we propose a framework to use CNN in distance education system. Thirdly, we carry out experiment on a data set that consists of facial expression images of learners to evaluate the performance of proposed method. Finally, we get the conclusion the average accuracy of CNN to recognize the facial expression is 93.63%. The high accuracy shows the application of CNN to recognize facial expression is valuable and helpful for the teachers in the future distance education system to attain and understand the learners’ emotion state in real time, accordingly to regulate the teaching strategy in time.

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PB - Springer Verlag

ER -

Sun A, Li Y, Huang Y-M, Li Q. The exploration of facial expression recognition in distance education learning system. In Lin L, Wu T-T, Huang Y-M, Huang Y-M, Starcic AI, Shadieva R, editors, Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings. Springer Verlag. 2018. p. 111-121. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-99737-7_11