Micro-expression recognition by leveraging color space information |
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Author: | Tang, Minghao1; Zong, Yuan2; Zheng, Wenming2; |
Organizations: |
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 2Key of Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China 3Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
4School of Electrical and Information Engineering, Yantai University, Yantai 90014, China
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Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.1 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202001152200 |
Language: | English |
Published: |
Institute of Electronics, Information and Communication Engineers,
2019
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Publish Date: | 2020-01-15 |
Description: |
AbstractMicro-expression is one type of special facial expressions and usually occurs when people try to hide their true emotions. Therefore, recognizing micro-expressions has potential values in lots of applications, e.g., lie detection. In this letter, we focus on such a meaningful topic and investigate how to make full advantage of the color information provided by the micro-expression samples to deal with the micro-expression recognition (MER) problem. To this end, we propose a novel method called color space fusion learning (CSFL) model to fuse the spatiotemporal features extracted in different color space such that the fused spatiotemporal features would be better at describing micro-expressions. To verify the effectiveness of the proposed CSFL method, extensive MER experiments on a widely-used spatiotemporal micro-expression database SMIC is conducted. The experimental results show that the CSFL can significantly improve the performance of spatiotemporal features in coping with MER tasks. see all
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Series: |
IEICE transactions on information and systems |
ISSN: | 0916-8532 |
ISSN-E: | 1745-1361 |
ISSN-L: | 0916-8532 |
Volume: | E102D |
Issue: | 6 |
Pages: | 1222 - 1226 |
DOI: | 10.1587/transinf.2018EDL8220 |
OADOI: | https://oadoi.org/10.1587/transinf.2018EDL8220 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Copyright information: |
© 2019 The Institute of Electronics, Information and Communication Engineers. |