Research on a Microexpression Recognition Technology Based on Multimodal Fusion

Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexp...

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Autores principales:	Jie Kang, Xiao Ying Chen, Qi Yuan Liu, Si Han Jin, Cheng Han Yang, Cong Hu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/cec08454bf1b4ca6bc7a9c97db54510d
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id	oai:doaj.org-article:cec08454bf1b4ca6bc7a9c97db54510d
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spelling	oai:doaj.org-article:cec08454bf1b4ca6bc7a9c97db54510d2021-11-29T00:55:31ZResearch on a Microexpression Recognition Technology Based on Multimodal Fusion1099-052610.1155/2021/5221950https://doaj.org/article/cec08454bf1b4ca6bc7a9c97db54510d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5221950https://doaj.org/toc/1099-0526Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexpression recognition work. In this paper, we propose a microexpression recognition method based on multimodal fusion through a comparative study of traditional microexpression recognition algorithms such as LBP algorithm and CNN and LSTM deep learning algorithms. The method couples the separate microexpression image information with the corresponding body temperature information to establish a multimodal fusion microexpression database. This paper firstly introduces how to build a multimodal fusion microexpression database in a laboratory environment, secondly compares the recognition accuracy of LBP, LSTM, and CNN + LSTM networks for microexpressions, and finally selects the superior CNN + LSTM network in the comparison results for model training and testing on the test set under separate microexpression database and multimodal fusion database. The experimental results show that a microexpression recognition method based on multimodal fusion designed in this paper is more accurate than unimodal recognition in multimodal recognition after feature fusion, and its recognition rate reaches 75.1%, which proves that the method is feasible and effective in improving microexpression recognition rate and has good practical value.Jie KangXiao Ying ChenQi Yuan LiuSi Han JinCheng Han YangCong HuHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Jie Kang Xiao Ying Chen Qi Yuan Liu Si Han Jin Cheng Han Yang Cong Hu Research on a Microexpression Recognition Technology Based on Multimodal Fusion
description	Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexpression recognition work. In this paper, we propose a microexpression recognition method based on multimodal fusion through a comparative study of traditional microexpression recognition algorithms such as LBP algorithm and CNN and LSTM deep learning algorithms. The method couples the separate microexpression image information with the corresponding body temperature information to establish a multimodal fusion microexpression database. This paper firstly introduces how to build a multimodal fusion microexpression database in a laboratory environment, secondly compares the recognition accuracy of LBP, LSTM, and CNN + LSTM networks for microexpressions, and finally selects the superior CNN + LSTM network in the comparison results for model training and testing on the test set under separate microexpression database and multimodal fusion database. The experimental results show that a microexpression recognition method based on multimodal fusion designed in this paper is more accurate than unimodal recognition in multimodal recognition after feature fusion, and its recognition rate reaches 75.1%, which proves that the method is feasible and effective in improving microexpression recognition rate and has good practical value.
format	article
author	Jie Kang Xiao Ying Chen Qi Yuan Liu Si Han Jin Cheng Han Yang Cong Hu
author_facet	Jie Kang Xiao Ying Chen Qi Yuan Liu Si Han Jin Cheng Han Yang Cong Hu
author_sort	Jie Kang
title	Research on a Microexpression Recognition Technology Based on Multimodal Fusion
title_short	Research on a Microexpression Recognition Technology Based on Multimodal Fusion
title_full	Research on a Microexpression Recognition Technology Based on Multimodal Fusion
title_fullStr	Research on a Microexpression Recognition Technology Based on Multimodal Fusion
title_full_unstemmed	Research on a Microexpression Recognition Technology Based on Multimodal Fusion
title_sort	research on a microexpression recognition technology based on multimodal fusion
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/cec08454bf1b4ca6bc7a9c97db54510d
work_keys_str_mv	AT jiekang researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion AT xiaoyingchen researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion AT qiyuanliu researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion AT sihanjin researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion AT chenghanyang researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion AT conghu researchonamicroexpressionrecognitiontechnologybasedonmultimodalfusion
_version_	1718407797460172800

Research on a Microexpression Recognition Technology Based on Multimodal Fusion

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