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首页> 外文期刊>Journal of Organizational and End User Computing >A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition
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A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition

机译：一种基于阈值的并行特征融合和特征选择的机器学习方法，用于自动步态识别

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摘要

Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.
机译：步态是机器学习领域中用于人类识别的重要生物识别过程。在本文中，基于准确的分割和多级特征提取，实现了一种用于步态识别的新方法。执行四个主要步骤：a）通过使用HSI颜色空间进行线性变换来增强帧中的运动区域; b）基于光流和背景减法的并行实现的感兴趣区域（ROI）检测; c）提取形状和几何特征并进行平行融合; d）多类支持向量机（MSVM）用于识别。提出的方法降低了错误率并增加了CCR。对三个数据集（CASIA-A，CASIA-B和CASIA-C）进行了广泛的实验，这三个数据集显示了衣服和携带条件的不同变化。所提出的方法在CASIA-A上的最大识别结果分别为98.6％，在CASIA-B上的93.5％和在CASIA-C上的97.3％。

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