基于核主成分分析的步态识别方法

为了从多帧步态序列中更有效地提取步态特征并实时性地进行身份识别,提出一种有效的基于平均步态能量图(MGEI)的核主成分分析(KPCA)的身份识别方法。通过预处理技术提取出运动人体的侧面轮廓,根据步态下肢的摆动距离统计出步态周期,得到MGEI。KPCA采用非线性方法提取主成分,描述待识别图像中多个像素之间的相关性。利用KPCA的方法在高维空间对MGEI提取特征,选择合适的核函数,用方差倒数加权欧氏距离进行身份识别。实验结果表明,该算法具有较好的识别性能,并且耗时大大缩短。

Gait recognition method based on kernel principal component analysis

Concerning the issue of extracting features more efficiently from a sequence of gait frames and real-time recognition, an effective human recognition method based on Mean Gait Energy Image (MGEI) was described, which utilized Kernel Principal Component Analysis (KPCA). A pre-processing technique was used to segment the moving silhouette from the walking figure. The algorithm obtained the gait quasi-periodicity through analyzing the width information of the lower limbs' gait contour edge, and the MGEI was calculated from gait period. KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images. In this paper, KPCA could make use of the high correlation between different MGEIs for feature extraction by selecting the proper kernel function, and Euclidean distance weighted by variance reciprocal was designed as the classifier. The experimental results show that the proposed approach has better recognition performance and the computation time is greatly reduced.