TY - GEN
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
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
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.
KW - Convolutional neural network
KW - Distance education system
KW - Emotion detection
KW - Facial expression recognition
UR - https://www.scopus.com/pages/publications/85053283150
U2 - 10.1007/978-3-319-99737-7_11
DO - 10.1007/978-3-319-99737-7_11
M3 - 会议稿件
AN - SCOPUS:85053283150
SN - 9783319997360
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 121
BT - Innovative Technologies and Learning - First International Conference, ICITL 2018, Proceedings
A2 - Lin, Lin
A2 - Wu, Ting-Ting
A2 - Huang, Yueh-Min
A2 - Huang, Yueh-Min
A2 - Starcic, Andreja Istenic
A2 - Shadieva, Rustam
PB - Springer Verlag
T2 - 1st International Conference on Innovative Technologies and Learning, ICITL 2018
Y2 - 27 August 2018 through 30 August 2018
ER -