基于独立成分分析和信息融合的步态识别  

Gait Recognition Based on Independent Component Analysis and Information Fusion

作　　者：鲁继文[1] 张二虎[1] 薛延学[1] 

机构地区：[1]西安理工大学信息科学系,西安710048

出　　处：《模式识别与人工智能》2007年第3期365-370,共6页Pattern Recognition and Artificial Intelligence

基　　金：陕西省自然科学基础研究计划资助项目(No.2006F26)

摘　　要：提出基于独立成分分析和多视角信息融合的步态识别方法.应用背景差分和阴影消除检测出人体步态轮廓,对人体轮廓用小波描述子进行特征提取.通过独立成分分析对特征进行压缩,应用支持向量机完成对步态的分类与识别.通过融合不同视角下的步态特征,完成多视角下信息融合的步态识别.方法在 NLPR 和 XAUT 步态数据库上进行实验,取得较高的识别率.实验结果表明本文方法具有较高的识别性能.A gait recognition method is proposed based on independent component analysis/support vector machine （ICA/SVM） and information fusion from multiple views. Human silhouette extraction is obtained by background subtraction and shadow elimination . Wavelet descriptor is applied to describe these silhouettes. Then, independent component analysis is employed to compress and extract their features, and gait classification is performed by support vector machine. The gait features from multiple views are fused, and recognition is finished. The method is evaluated on the National Laboratory of Pattern Recognition （NLPR） and Xi＇ an University of Technology （XAUT） gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.

关 键 词：步态识别 独立成分分析(ICA) 支持向量机 信息融合 小波描述子 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]