Journal
TRAITEMENT DU SIGNAL
Volume 36, Issue 6, Pages 557-563Publisher
INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
DOI: 10.18280/ts.360611
Keywords
convolutional neural network (CNN); micro-expression recognition; deep learning; face detection; classroom learning
Funding
- National Natural Science Foundation of China [71871106]
- Jiangsu Provincial Social Science Foundation [18GLD014]
- Fundamental Research Funds for the Central Universities [2019JDZD06]
- Chinese University Research Foundation for Young Scholars [JUSRP11882]
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In classroom teaching, the teachers should adjust the teaching strategy and improve the teaching effect based on the expression and learning state of each student. This paper mainly develops a micro-expression recognition algorithm for students in classroom learning, based on convolutional neural network (CNN) and automatic face detection. Specifically, the multitask deep convolution network (DNN) was adopted to detect the landmark points of human face, and a hybrid DNN was designed to extract the optical-flow features of micro-expression. The extracted features were improved through redundancy removal and dimensionality reduction. The rationality of our algorithm was proved through a comparative experiment on real-world databases and an application in classroom teaching. The research results provide a new direction for applying deep learning in face recognition.
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