论文标题
步态识别使用3-D人体形状推理
Gait Recognition Using 3-D Human Body Shape Inference
论文作者
论文摘要
步态识别是根据个人的步行模式来识别个人的，它是一种重要的生物识别技术，因为可以从远处观察到它，并且不需要受试者的合作。由于人类轮廓序列的外观变体，由于不同的视角，携带物体和衣服而产生的人类轮廓序列中的外观变体很困难。最近的研究产生了多种应对这些变体的方法。在本文中，我们介绍了从有限图像中推断出的3-D身体形状的用法，这些图像原则上是指定变体的不变。 3-D形状的推断是一项艰巨的任务，尤其是当数据集中仅提供轮廓时。我们提供了一种通过从RGB照片中转移到3D形状的知识来学习3-D身体推断的方法。我们在多个现有的最新步态基线上使用我们的方法，并在两个公共数据集Casia-B和OUMVLP上获得步态识别的一致改进，在几种变体和设置上，包括在训练过程中看不到的新型新颖观点的新设置。
Gait recognition, which identifies individuals based on their walking patterns, is an important biometric technique since it can be observed from a distance and does not require the subject's cooperation. Recognizing a person's gait is difficult because of the appearance variants in human silhouette sequences produced by varying viewing angles, carrying objects, and clothing. Recent research has produced a number of ways for coping with these variants. In this paper, we present the usage of inferring 3-D body shapes distilled from limited images, which are, in principle, invariant to the specified variants. Inference of 3-D shape is a difficult task, especially when only silhouettes are provided in a dataset. We provide a method for learning 3-D body inference from silhouettes by transferring knowledge from 3-D shape prior from RGB photos. We use our method on multiple existing state-of-the-art gait baselines and obtain consistent improvements for gait identification on two public datasets, CASIA-B and OUMVLP, on several variants and settings, including a new setting of novel views not seen during training.
