标题:Macro-to-micro transformation model for micro-expression recognition
作者:Jia, Xitong; Ben, Xianye; Yuan, Hui; Kpalma, Kidiyo; Meng, Weixiao
作者机构:[Jia, Xitong; Ben, Xianye; Yuan, Hui] Shandong Univ, Sch Informat Sci & Engn, 27 Shanda South Rd, Jinan 250100, Shandong, Peoples R China.; [Kpalma, 更多
通讯作者:Ben, Xianye
通讯作者地址:[Ben, XY]Shandong Univ, Sch Informat Sci & Engn, 27 Shanda South Rd, Jinan 250100, Shandong, Peoples R China.
来源:JOURNAL OF COMPUTATIONAL SCIENCE
出版年:2018
卷:25
页码:289-297
DOI:10.1016/j.jocs.2017.03.016
关键词:Micro-expression recognition; Macro-to-micro transformation model;; Feature selection; Singular value decomposition
摘要:As one of the most important forms of psychological behaviors, micro-expression can reveal the real emotion. However, the existing labeled training samples are limited to train a high performance model. To overcome this limit, in this paper we propose a macro-to-micro transformation model which enables to transfer macro-expression learning to micro-expression. Doing so improves the efficiency of the micro expression features. For this purpose, LBP and LBP-TOP are used to extract macro-expression features and micro-expression features, respectively. Furthermore, feature selection is employed to reduce redundant features. Finally, singular value decomposition is employed to achieve macro-to-micro transformation model. The experimental evaluation based on the incorporated database including CK+ and CASME2 demonstrates that the proposed model achieves a competitive performance compared with the existing micro-expression recognition methods. (C) 2017 Elsevier B.V. All rights reserved.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:4
Scopus被引频次:4
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016400510&doi=10.1016%2fj.jocs.2017.03.016&partnerID=40&md5=7ebf486b52ee19a3bcf1fd197abfabde
TOP