标题:Multilinear mean component analysis for gait recognition
作者:Tian, Yawei ;Ben, Xianye ;Zhang, Peng ;Sun, Menglei
作者机构:[Tian, Yawei ;Ben, Xianye ;Zhang, Peng ;Sun, Menglei ] School of Information Science and Engineering, Shandong University, Jinan, 250100, China
会议名称:26th Chinese Control and Decision Conference, CCDC 2014
会议日期:31 May 2014 through 2 June 2014
来源:26th Chinese Control and Decision Conference, CCDC 2014
出版年:2014
页码:2632-2637
DOI:10.1109/CCDC.2014.6852618
关键词:Eigenvalues; Gait Recognition; Mean Vector; Multilinear Mean Component Analysis
摘要:In this paper multilinear mean component analysis (MMCA) is introduced as a new algorithm for gait recognition. Compared with traditional PCA and MPCA, the new method MMCA is based on dimensionality reduction by preserving the squared length, and implicitly also the direction of the mean vector of the each mode's original input. The solution is not necessarily corresponding to the top eigenvalues. MMCA improved the clustering results and reduced the small sample size (SSS) problem and has great convergence. MMCA as a feature extraction tool provides stable recognition rates and the MMCA-based approaches we proposed achieves better performance for gait recognition based on the University of South Florida (USF) HumanID Database. © 2014 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905245406&doi=10.1109%2fCCDC.2014.6852618&partnerID=40&md5=002db566eb2376cd55876eee1717b384
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