基于参数正则空间的步态识别

P.S. Huang, C.J. Harris, M.S. Nixon
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引用次数: 173

摘要

基于主成分分析(PCA)的特征空间变换(EST)在模板匹配的自动人脸识别和步态分析中是一种有效的度量方法,但不需要使用数据分析来提高分类能力。步态是一种新的生物识别技术,旨在通过人们走路的方式来识别他们。在本文中,我们提出了一种将基于典型分析(CA)的典型空间变换(CST)与EST相结合的特征提取方法。该方法可以降低数据维数,同时优化不同步态类的可分离性。每个图像模板从高维图像空间投影到低维正则空间。采用模板匹配的方法,使步态识别的准确性和鲁棒性大大提高。在一个小型数据库上的实验结果表明,使用这种方法可以通过步态100%准确地识别受试者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognising humans by gait via parametric canonical space

Based on principal component analysis (PCA), eigenspace transformation (EST) was demonstrated to be a potent metric in automatic face recognition and gait analysis by template matching, but without using data analysis to increase classification capability. Gait is a new biometric aimed to recognise subjects by the way they walk. In this article, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with EST for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously. Each image template is projected from the high-dimensional image space to a low-dimensional canonical space. Using template matching, recognition of human gait becomes much more accurate and robust in this new space. Experimental results on a small database show how subjects can be recognised with 100% accuracy by their gait, using this method.

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