The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ekspresi mikro ialah salah satu jenis ekspresi muka istimewa dan biasanya berlaku apabila orang cuba menyembunyikan emosi sebenar mereka. Oleh itu, mengiktiraf ekspresi mikro mempunyai nilai berpotensi dalam banyak aplikasi, contohnya, pengesanan pembohongan. Dalam surat ini, kami menumpukan pada topik yang begitu bermakna dan menyiasat cara memanfaatkan sepenuhnya maklumat warna yang disediakan oleh sampel ekspresi mikro untuk menangani masalah pengecaman ekspresi mikro (MER). Untuk tujuan ini, kami mencadangkan kaedah baru yang dipanggil model pembelajaran gabungan ruang warna (CSFL) untuk menggabungkan ciri spatiotemporal yang diekstrak dalam ruang warna yang berbeza supaya ciri spatiotemporal yang digabungkan akan menjadi lebih baik dalam menerangkan ekspresi mikro. Untuk mengesahkan keberkesanan kaedah CSFL yang dicadangkan, eksperimen MER yang meluas pada pangkalan data ekspresi mikro spatiotemporal yang digunakan secara meluas SMIC dijalankan. Keputusan eksperimen menunjukkan bahawa CSFL boleh meningkatkan prestasi ciri spatiotemporal dengan ketara dalam menangani tugas MER.
Minghao TANG
Jiangsu University
Yuan ZONG
Southeast University
Wenming ZHENG
Southeast University
Jisheng DAI
Jiangsu University
Jingang SHI
University of Oulu
Peng SONG
Yantai University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Minghao TANG, Yuan ZONG, Wenming ZHENG, Jisheng DAI, Jingang SHI, Peng SONG, "Micro-Expression Recognition by Leveraging Color Space Information" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 6, pp. 1222-1226, June 2019, doi: 10.1587/transinf.2018EDL8220.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8220/_p
Salinan
@ARTICLE{e102-d_6_1222,
author={Minghao TANG, Yuan ZONG, Wenming ZHENG, Jisheng DAI, Jingang SHI, Peng SONG, },
journal={IEICE TRANSACTIONS on Information},
title={Micro-Expression Recognition by Leveraging Color Space Information},
year={2019},
volume={E102-D},
number={6},
pages={1222-1226},
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.},
keywords={},
doi={10.1587/transinf.2018EDL8220},
ISSN={1745-1361},
month={June},}
Salinan
TY - JOUR
TI - Micro-Expression Recognition by Leveraging Color Space Information
T2 - IEICE TRANSACTIONS on Information
SP - 1222
EP - 1226
AU - Minghao TANG
AU - Yuan ZONG
AU - Wenming ZHENG
AU - Jisheng DAI
AU - Jingang SHI
AU - Peng SONG
PY - 2019
DO - 10.1587/transinf.2018EDL8220
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2019
AB - 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.
ER -