基于子模式的完全二维主成分分析的步态识别算法

Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition

提出一种基于子模式的完全二维主成分分析的步态识别算法.首先对步态能量图进行子块划分,自适应地去掉对分类无用的子块.然后分别对每个子图像采用完全二维主成分分析方法进行特征抽取.最后将各个子块的特征合为整体采用最近邻分类器来测试识别.应用上述方法在CASIA步态数据库上进行实验,通过实验确定分块数目.实验结果表明本文算法明显好于完全二维主成分分析方法,不但有利于提取局部特征,而且对外套变化、背包,行走方向变化的步态识别也较有效.

A gait recognition method based on subpattern complete two dimensional principal component analysis （SpC2DPCA） is proposed. Firstly, gait energy images are divided into small sub-images and any ineffectual subblock is removed adaptively. Then, C2DPCA approach is applied to every sub-image directly to obtain sub-feature. Finally, those sub-features are synthesized into the whole for subsequent classification using the nearest neighbor classifier. The proposed gait recognition method is evaluated on the CASIA gait database, and the number of sub-pattern division is determined through experiments. The experimental results demonstrate that the performance of SpC2DPCA is obviously superior to that of C2DPCA. The proposed method is effective in local feature extraction and person identification with clothes changing, backpacking and direction of gait changing.