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Robust gait identification using Kinect dynamic skeleton data

机译：使用Kinect动态骨架数据进行稳健的步态识别

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摘要

Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an automatic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenarios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.
机译：步态最近被提出作为一种生物特征，相对于其他人类特征，步态可以在远处被捕获而无需被观察对象的协作。因此，在多种情况下，例如对于身份验证，这是一种有希望的人员识别方法。访问控制和取证应用。在本文中，我们提出了一种自动步态识别系统，该系统基于使用流行的Kinect传感器提供的3D骨骼跟踪功能获取的一组特征。步态特征是根据所选关节组之间的距离及其相对于行走方向的垂直和横向摆动来定义的。此外，我们不依赖于传感器位置的任何几何假设。使用主成分分析和支持向量机，基于监督分类对人员进行识别时，可以显示定义的步态特征的有效性。在以下两种情况下提供了一组丰富的实验：分别是受控标识设置和经典视频监视设置。此外，我们通过比较间隔三年的几名受试者的步态样本，调查是否至少在几年的时间间隔内，步态是否可以视为个体不变。我们的实验分析表明，所提出的方法对于采集设置具有鲁棒性，并且相对于现有技术而言，具有非常具有竞争力的识别精度。

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