基于提升5/3小波和主成分分析的步态性别分类

Omer Mohammed Salih Hassan, Adnan Mohsin Abdulazeez, V. Tiryaki
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引用次数: 22

摘要

本研究描述了一种步态外观的表征,用于人的识别和分类。步态表示是基于小波5/3提升方案的简单特征,如从人体行走运动的视频轮廓中提取的特征。不管它的毫不费力,这可能会导致我们说,结果特征向量包含足够的信息,可以很好地完成人类识别和性别分类任务。研究了在不同的识别任务下,不同方法对总特征随时间函数的识别行为。除此之外,我们还使用(C4.5算法)提供基于步态外观特征的性别分类结果。因此,CASIA - B步态数据库的分类率为97.98%,OU-ISIR步态数据库大人口数据集的识别率为97.5%,这些结果都是由性别分类数据得出的。步态数据库表明,该方法比文献中大多数现有方法具有更好的识别性能,特别是在某些步行变化情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis
This study describes a representation of gait appearance for the purpose of person identification and classification. The gait representation is based on wavelet 5/3 lifting scheme simple features such as features extracted from video silhouettes of human walking motion. Regardless of its effortlessness, this may lead us to say that, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We found out the recognition behaviors of different methods to total features over time functions under different recognition tasks. In addition to that, we provide results of gender classification based on our gait appearance features using a (C4.5 algorithm). So, the result of classification rate for CASIA - B gait databases is 97.98% and the result of recognition rate for OU-ISIR gait Database Large Population Dataset is 97.5%, these results have been obtained from gender classification data. Gait database demonstrates that the proposed method achieves better recognition performance than the most existing methods in the literature, and particularly under certain walking variations.
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