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Spatio-temporal metric learning for individual recognition from locomotion

机译：时空度量学习，用于单一的单个识别

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Individual recognition from locomotion is a challenging task owing to large intra-class and small inter-class variations. In this article, we present a novel metric learning method for individual recognition from skeleton sequences. Firstly, we propose to model articulated body on Riemannian manifold to describe the essence of human motion, which can reflect biometric signatures of the enrolled individuals. Then two spatia-temporal metric learning approaches are proposed, namely Spatio-Temporal Large Margin Nearest Neighbor (ST-LMNN) and Spatio-Temporal Multi-Metric Learning (STMM), to learn discriminant bilinear metrics which can encode the spatio-temporal structure of human motion. Specifically, the STLMNN algorithm extends the bilinear model into classical Large Margin Nearest Neighbor method, which learns a low-dimensional local linear embedding in the spatial and temporal domain, respectively. To further capture the unique motion pattern for each individual, the proposed STMM algorithm learns a set of individual-specific spatio-temporal metrics, which make the projected features of the same person closer to its class mean than that of different classes by a large margin. Beyond that, we present a new publicly available dataset for locomotion recognition to evaluate the influence of both internal and external covariant factors. According to the experimental results from the three public datasets, we believe that the proposed approaches are both able to achieve competitive results in individual recognition. (C) 2020 Elsevier Inc. All rights reserved.
机译：由于阶级内部和阶级的阶级跨阶级变化，机职的个人识别是一个具有挑战性的任务。在本文中，我们提出了一种新的公制学习方法，用于从骨架序列中的个人识别。首先，我们建议在riemannian歧管上模拟铰接式的身体来描述人类运动的本质，这可以反映已注册的人的生物识别。然后提出了两种时空度量学习方法，即时空大型裕度最近邻（ST-LMNN）和时空多度量学习（STMM），以学习可以编码的判别双线性指标，其可以编码时空结构人类运动。具体地，STLMNN算法将双线性模型扩展到经典大边缘邻近邻方法，其分别在空间和时间域中学习低维本地线性嵌入。为了进一步捕获每个人的唯一运动模式，所提出的STMM算法了解一组特定的时空度量，使得与其级别的同一个人的预定特征使得比大边距的不同类别。除此之外，我们提供了一个新的公开可用的数据集，用于当地情观识别，以评估内部和外部协变量的影响。根据三个公共数据集的实验结果，我们认为，拟议的方法都能够在个人认可中实现竞争结果。 （c）2020 Elsevier Inc.保留所有权利。

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