Veeraraghavan Ashok, Roy-Chowdhury Amit K, Chellappa Rama
Center for Automation Research, #4417 A V Williams Building, University of Maryland at College Park, College Park, MD 20742, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Dec;27(12):1896-909. doi: 10.1109/TPAMI.2005.246.
We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall's definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on Dynamic Time-Warping. We suggest a modification of the Dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication.
我们提出了一种使用参数模型和非参数方法来比较两个变形形状序列的方法。在我们的方法中，肯德尔形状定义用于特征提取。由于形状特征基于非欧几里得流形，我们在切空间上提出了自回归模型和自回归移动平均模型等参数模型，并通过基于步态的人体识别实验证明了这些模型捕捉形状变形本质的能力。非参数模型基于动态时间规整。我们建议对动态时间规整算法进行修改，以纳入形状变形发生的非欧几里得空间的性质。我们还通过将该算法应用于基于步态的人体识别来展示其有效性。我们将人的轮廓形状变形作为一种区分特征加以利用，并使用非参数模型提供识别结果。我们的分析得出了一些关于形状和运动学在基于自动步态的人员认证中的作用的有趣观察结果。
