Multilinear Mean Component Analysis for Gait Recognition

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 modes 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.

关键词： Multilinear Mean Component Analysis Gait Recognition Eigenvalues Mean Vector

作者: Yawei Tian Xianye Ben Peng Zhang Menglei Sun

作者单位: School of Information Science and Engineering,Shandong University,Jinan,250100,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 2632-2637

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）