Gait recognition based on KDA and SVM

摘要：

In this paper, we used gait silhouettes that provided by CASIA, and all we study are based on this database. Firstly, we normalized and centralized gait silhouettes and get the gait sequence, secondly, we extract the active regions by calculating the difference of two adjacent silhouettes images, and construct an AEI by accumulating these active regions, finally, using Kernel Discriminant Analysis (KDA) method to analysis the AEI, and parameter optimization method used to determine the nuclear function of KDA, and using SVM to classified and recognized gait. Experimental results show that such methods to be identified effective.

关键词： KDA AEI FDEI SVM Eigenvector

作者: Qi Yang Yali Tian

作者单位: School of Mechanical Engineering Shenyang Ligong University Shenyang,China China northeast architectural design and research institute shenzhen branch shenzhen, China

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 160-163

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）