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Gait Recognition Based On the Feature Fusion

机译:基于特征融合的步态识别

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摘要

A gait recognition algorithm is proposed that fuses motion and static features of sequences of silhouette images--the wavelet moment and the widths capture the motion and static characteristic of gait. A subspace transformation, Principal Component Analysis(PCA),is applied to process the spatial templates. It aims essentially at reducing data dimensionalities. Finally, nearest neighbor classifier is adopted to recognize subjects. Experimental results show that the method is efficient for human identification, and has a recognition rate of around 88% on the CASIA data set, furthermore, the performance is compared with other algorithms.
机译:提出了一种步态识别算法,该算法融合了剪影图像序列的运动和静态特征-小波矩和宽度捕获了步态的运动和静态特征。一个子空间变换,主成分分析(PCA),被用来处理空间模板。它的主要目的是减少数据维数。最后,采用最近邻分类器进行主题识别。实验结果表明,该方法可有效识别人,在CASIA数据集上的识别率约为88%,并与其他算法进行了比较。

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