@inproceedings{cbc6de6f62c54396a987b53d99ef9232,
title = "Microexpression recognition based on improved robust principal component analysis and texture feature extraction",
abstract = "Micro-expression can reflect the emotional information that humans can hardly conceal. There are three main characteristics: short duration, low intensity and local movement. From these characteristics it can be seen that the motion of the microexpression is sparse. For sparse micro-expression movement, a robust principal component analysis (RPCA) was proposed to extract subtle micro-expression motion information. Using improved Edge Direction Histogram (EOH) algorithm and Binary Gradient Contours (BGC) algorithm to extract local texture features can solve the problem of spatio-temporal domain and obtain high recognition accuracy. Experiments on the SMIC database show that the proposed algorithm has better performance.",
keywords = "BGC algorithm, EOH algorithm, Recognition accuracy, Robust principal component analysis",
author = "Dong Xiaochen and Zhao Zhigang and Li Qiang",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 4th International Conference on Communication and Information Processing, ICCIP 2018 ; Conference date: 02-11-2018 Through 04-11-2018",
year = "2018",
month = nov,
day = "2",
doi = "10.1145/3290420.3290421",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "48--53",
booktitle = "ICCIP 2018 - Proceedings of 2018 4th International Conference on Communication and Information Processing",
address = "美国",
}