University of Oulu

TANG, M., ZONG, Y., ZHENG, W., DAI, J., SHI, J., & SONG, P. (2019). Micro-Expression Recognition by Leveraging Color Space Information. IEICE Transactions on Information and Systems, E102.D(6), 1222–1226. https://doi.org/10.1587/transinf.2018edl8220

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
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
Publish Date: 2020-01-15
Description:

Abstract

Micro-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.

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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.