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基于模糊理论决策的双向二维PCA 步态识别算法

  

  • 出版日期:2012-12-31 发布日期:2015-07-29

A gait recognition algorithm using fuzzy theory decision double-directional two-dimensional principal component analysis

  • Online:2012-12-31 Published:2015-07-29

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摘要： ：针对步态识别中的平均步态能量图像系数矩阵维数过高和分类较困难的特
点，提出一种基于模糊理论决策分类的双向二维主成分分析的步态识别算法。通过预处理技
术得到平均步态能量图并将得到的图像分割为多个子图像，利用双向二维主成分分析来降低
平均步态能量子图像的系数矩阵维数，加快识别速度。引入模糊理论决策的方法进行最近邻
分类器的分类。最后在CASIA 步态数据库上对所提出的算法进行实验，实验结果表明该算
法具有较好的识别性能并有较强的鲁棒性。

关键词: 步态识别, 平均步态能量图, 主成分分析, 双向二维主成分分析

Abstract: A gait recognition algorithm is proposed based on fuzzy theory classification
decision double-directional two-dimensional principal component analysis (DTPCA) for the
problems of high dimensionality and difficult classification of mean gait energy image (MGEI)
coefficient matrix. A preprocess technique is used to obtain MGEI, which is then divided into
some of sub-image blocks, then DTPCA is used to reduce the dimension of mean gait energy
sub-image blocks coefficient matrix and improve the recognition speed. The fuzzy theory is
introduced to classify the nearest-neighbour classifiers. The proposed algorithm is evaluated on
CASIA Gait Database. Experimental results show that the proposed approach achieves a high
recognition accuracy and stronger robustness.

Key words: gait recognition, mean gait energy image (MGEI), principal component analysis
(PCA), double-directional two-dimensional principal component analysis (DTPCA)