Learning Expression Recognition Based On Image Sequence

Authors

Suqin Wang, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;;
Zhang Feng, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;;
Yudou Gao, 2. Information center of Yunnan Power Grid Co., Ltd, Kunming 650200, China;
Shi Min, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;;

Abstract

Abstract: By recognizing the facial expressions, the emotional state of learner can be judged and their learning effect can be analyzed. Due to the persistence and timing of facial expressions, the sequence of facial images is adopted as the object of facial expression recognition. The Long Short Term Memory Network (LSTM) and VGGNet are combined into a VGGNET-LSTM model. On this basis, facial expression recognition is carried out, which significantly improves the accuracy of recognition. Based on the transfer learning method, VGGNet is transferred to the learning expression data set after being pre-trained through the basic expression data set CK+ and avoids the defect of insufficient data in the learning expression data set and solves the problem of overfitting the model.

Recommended Citation

Wang, Suqin; Feng, Zhang; Gao, Yudou; and Min, Shi (2020) "Learning Expression Recognition Based On Image Sequence," Journal of System Simulation: Vol. 32: Iss. 7, Article 13.
DOI: 10.16182/j.issn1004731x.joss.19-VR0470
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss7/13

First Page

1322

Revised Date

2019-12-12

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-VR0470

Last Page

1330

CLC

TP391.4

Recommended Citation

Wang Suqin, Zhang Feng, Gao Yudou, Shi Min. Learning Expression Recognition Based On Image Sequence[J]. Journal of System Simulation, 2020, 32(7): 1322-1330.

DOI

10.16182/j.issn1004731x.joss.19-VR0470