A Three-Way AutoCorrelation Based Approach to Human Identification by Gait
A Three-Way AutoCorrelation Based Approach to Human Identification by Gait
复制标题
基于三向自相关的人体步态识别方法
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
N. Otsu
中科院分区：
文献类型：
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作者：
Takumi Kobayashi;N. Otsu
我们提出了一个方案，步态识别使用立方高阶局部自相关（CHLAC），判别分析，和k-NN决策规则。CHLAC是基于运动图像中像素的三向（x-，y-和时间维）自相关性，并且它有效地提取运动特征。该方法有几个属性优选的识别：平移不变性（渲染的方法分割自由）和鲁棒性的数据中的噪声。此外，该方法是如此普遍，既不使用先验知识，也不使用人体形状等物体的几何学，适用于任何三路数据。我们通过引入步态的一些知识来优化CHLAC中的参数，使该方案更有效地用于步态识别。我们的计划被应用到NIST步态数据集的人体识别，并与其他方法的结果进行了比较。我们的方案优于其他尽管简单的特征提取和简单的分类规则。
We propose a scheme for gait recognition using cubic higher-order local auto-correlation (CHLAC), discriminant analysis, and k-NN decision rules. CHLAC is based on three-way (x-, y-, and time-dimensional) auto-correlations of pixels in motion images, and it effectively extracts motion features. The method has several properties preferable for recognition: shift-invariance (rendering the method segmentation-free) and robustness to noise in data. Moreover, the method is so general as to use neither a priori knowledge nor heuristics about objects such as human shapes and is applicable to any three-way data. We made the scheme more effective for gait recognition by introducing some knowledge of gait to optimise parameters in CHLAC. Our scheme was applied to the NIST gait dataset for human identification, and the result was compared to those of other methods. Our scheme outperformed the others in spite of the simple feature extraction and the simple classification rule.
DOI：
10.1109/tpami.2005.39
发表时间：
2005-02-01
影响因子：
23.6
作者：
Sarkar, S;Phillips, PJ;Bowyer, KW
通讯作者：
Bowyer, KW