基于频谱分析的Procrustes统计步态识别算法  

A Procrustes statistical gait recognition algorithm based on spectrum analysis

作　　者：杨静[1,2] 阮秋琦[1] 李小利[1,2] 

机构地区：[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

出　　处：《智能系统学报》2011年第5期432-439,共8页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(60973060)

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.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.

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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]