标题:Multi linear Mean Component Analysis for Gait Recognition
作者:Tian, Yawei; Ben, Xianye; Zhang, Peng; Sun, Menglei
通讯作者:Tian, YW
作者机构:[Tian, Yawei; Ben, Xianye; Zhang, Peng; Sun, Menglei] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
会议名称:26th Chinese Control and Decision Conference (CCDC)
会议日期:MAY 31-JUN 02, 2014
来源:26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC)
出版年:2014
页码:2632-2637
关键词:Multilinear Mean Component Analysis; Gait Recognition; Eigenvalues; Mean; Vector
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.
收录类别:CPCI-S
WOS核心被引频次:1
资源类型:会议论文
TOP