Gait recognition using a view transformation model in the frequency domain
Gait recognition using a view transformation model in the frequency domain
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DOI：
10.1007/11744078_12
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发表时间：
2006-01-01
期刊：
影响因子：
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通讯作者：
Yagi, Yasushi
中科院分区：
文献类型：
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作者：
Makihara, Yasushi;Sagawa, Ryusuke;Yagi, Yasushi
步态分析最近获得了关注的方法，在距离相机的个人识别。然而，由于观察方向的变化而引起的外观变化给步态识别系统造成困难。在这里，我们提出了一种方法的步态识别从不同的角度来看，使用频域特征和视图转换模型。我们首先构建一个时空轮廓体积的步行的人，然后提取频域特征的傅立叶分析的基础上的步态周期。接下来，我们的视图转换模型是用来自多个视图方向的多个人的训练集获得的。在识别阶段，模型将图库特征转换为与输入特征相同的视图方向，因此特征彼此匹配。24个视角下的步态识别实验验证了该方法的有效性。
Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, appearance changes due to view direction changes cause difficulties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an input feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method.