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Expression detection based on a novel emotion recognition method

SCI-ECPCI-SEI
WOS被引频次:2
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成果类型:
期刊论文、会议论文
作者:
Xun Gong;Yong Yang;Jianhui Lin;Tianrui Li
通讯作者:
Gong, Xun
作者机构:
[Tianrui Li; Xun Gong] SW Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China.
[Yong Yang] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China.
[Jianhui Lin] SW Jiaotong Univ, Tract Power State Key Lab, Chengdu 610031, Peoples R China.
通讯机构:
[Gong, Xun] SW Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China.
语种:
英文
关键词:
emotion recognition;ensemble learning;rough set;data mining
期刊:
International Journal of Computational Intelligence Systems
ISSN:
1875-6891
年:
2011
卷:
4
期:
1
页码:
44-53
会议名称:
4th International Conference on Rough Sets and Knowledge Technology (RSKT)
会议时间:
JUL 14-16, 2009
会议地点:
Gold Coast, AUSTRALIA
会议主办单位:
[Gong, Xun;Li, Tianrui] SW Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China.^[Yang, Yong] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China.^[Lin, Jianhui] SW Jiaotong Univ, Tract Power State Key Lab, Chengdu 610031, Peoples R China.
会议赞助商:
Univ So Queensland, Queensland Univ Technol, Chongqing Univ Posts & Telecommun, Univ Regina, Routh Set & Soft Computat Soc Chinese Assoc Artificial Intelligence
出版地:
29 AVENUE LAUMIERE, PARIS, 75019, FRANCE
出版者:
ATLANTIS PRESS
文献类别:
WOS:Article;Proceedings Paper;EI:Journal article (JA)
所属学科:
ESI学科类别:计算机科学;WOS学科类别:Computer Science, Artificial Intelligence;Computer Science, Interdisciplinary Applications
入藏号:
WOS:000286636000005;EI:20110413628320
机构署名:
本校为第一且通讯机构
院系归属:
信息科学与技术学院
摘要:
As facial expression is an essential way to convey human's feelings, in this paper, a dynamic selection ensemble learning method is proposed to analyze their emotion automatically. A feature selection algorithm is proposed at first based on rough set and the domain oriented data driven data mining theory, which can get multiple reducts and candidate classifiers. Then the nearest neighborhood of each unseen sample is found in a validation subset and the most accurate classifier is extracted from the candidate classifiers. Finally, the selected classifier is used to recognize unseen samples. Experimental results show that the proposed method is effective and suitable for emotion recognition.
参考文献:
Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1007/BF00058655
*CQUPTE, CHONGQ U POSTS TEL E
Dietterich TG, 2000, MACH LEARN, V40, P139, DOI 10.1023/A:1007607513941
Ditterrich TG, 1997, ARTIF INTELL, V4, P97
DITTERRICH TG, 2001, LNCS, V1857, P1

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