[1]杨静,阮秋琦,李小利.基于频谱分析的Procrustes统计步态识别算法[J].智能系统学报,2011,6(05):432-439.
 YANG Jing,RUAN Qiuqi,LI Xiaoli,et al.A Procrustes statistical gait recognition algorithm based on spectrum analysis[J].CAAI Transactions on Intelligent Systems,2011,6(05):432-439.
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基于频谱分析的Procrustes统计步态识别算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第6卷
期数:
2011年05期
页码:
432-439
栏目:
出版日期:
2011-10-30

文章信息/Info

Title:
A Procrustes statistical gait recognition algorithm based on spectrum analysis
文章编号:
1673-4785(2011)05-0432-08
作者:
杨静12阮秋琦1李小利12
1.北京交通大学 信息科学研究所,北京 100044;
2.北京交通大学 现代信息科学与网络技术北京市重点实验室,北京100044
Author(s):
YANG Jing12 RUAN Qiuqi1 LI Xiaoli1 2
1.Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
2.Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China
关键词:
步态识别Procrustes 统计形状分析Procrustes 均值形状(PMS)傅里叶频谱分析(FSA)多视角识别
Keywords:
gait recognition Procrustes statistical shape analysis Procrustes mean shape (PMS) Fourier spectrum analysis (FSA) multiview recognition
分类号:
TP391.4
文献标志码:
A
摘要:
在信息安全越加重要的现代社会,步态识别以其特有的优势作为一种身份识别手段,得到了很多关注.提出一种基于Procrustes均值形状的傅里叶频谱分析(FSAOPMS)的适用于多视角的步态识别方法.利用Procrustes 统计形状分析方法将步态序列中人体轮廓的连续步态变化表示成一个紧致的Procrustes 均值形状(PMS),将PMS作为原始步态特征,对PMS进行傅里叶频谱分析(FSA).计算不同步态序列的PMS幅度谱的欧式距离,利用最近邻(NN)分类器进行识别.在中国科学院自动化所的CASIA Gait Database数据库上进行了实验,与其他3种方法进行了比较,新方法具有很高的识别率,证明了该算法的有效性.
Abstract:
As a special means of identification, gait recognition has acquired a lot of attention in a modern society in which information security has become increasingly important. A multiview gait recognition algorithm based on the Fourier spectrum analysis of Procrustes mean shape (FSAOPMS) was proposed in this paper. Procrustes shape analysis was used to produce a compact Procrustes mean shape (PMS) from the continuous gesture variation of human body contours in gait sequences. The spectrum of the PMS was analyzed using the Fourier transformation, and the Euclidean distance of the amplitude spectrum of the PMS from various sequences was computed. The classifier was the nearest neighbor (NN). The results of comparison with the other three methods in the CASIA database show that the proposed algorithm is more effective in terms of the recognition accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2011-02-28.
基金项目:国家自然科学基金资助项目(60973060). 
通信作者:杨静.E-mail:jingyangsxh@gmail.com.
作者简介:
杨静,女,1987年生,硕士研究生,主要研究方向为图像处理与模式识别.
阮秋琦,男,1944年生,教授,博士生导师,北京交通大学信息科学研究所所长,国务院学位委员会学科评议组成员,IEEE高级会员.主要研究方向为图像处理、计算机视觉、模式识别、虚拟现实.曾承担国家自然科学基金重大项目,国家自然科学基金项目,国家“863”项目,铁道部、省、市级科研项目50余项.曾获国家教委科技进步二等奖、铁道部科技进步二等奖和三等奖等.发表学术论文350余篇,出版著作3部,获国家专利1项.
 李小利,女,1986年生,博士研究生,主要研究方向为图像处理与模式识别.
更新日期/Last Update: 2011-11-16