Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait template which makes it difficult to preserve temporal information, or a gait sequence that maintains unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification. Based on this deep set perspective, our method is immune to frame permutations, and can naturally integrate frames from different videos that have been acquired under different scenarios, such as diverse viewing angles, different clothes, or different item-carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset. Under various complex scenarios, our model also exhibits a high level of robustness. It achieves accuracies of 90.8% and 70.3% on CASIA-B under bag-carrying and coat-wearing walking conditions respectively, significantly outperforming the best existing methods. Moreover, the proposed method maintains a satisfactory accuracy even when only small numbers of frames are available in the test samples; for example, it achieves 85.0% on CASIA-B even when using only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.

翻译：Gait 是一个独特的生物特征,可以在远处被识别;因此,它具有广泛的在预防犯罪、法医鉴定和社会保障方面的应用。 描绘步态,现有的步态识别方法使用难以保存时间信息的一个步态模板,或者保持不必要的顺序限制的步态序列,从而失去动作识别的灵活性。 在本文中,我们提出了一个新颖的观点,将步态用作一套深厚的集体,这意味着一组步态框架由一个全球-地方接合的深层网络集成,这个网络的灵感来自我们的左-右-右-hemisphere 处理信息以学习可用于识别的信息的方式。基于这一深层的视角,我们的方法对框架配置有免疫性,或者可以自然整合在不同情景下获得的不同视频的框,如不同视角、不同服装或不同物品跟踪条件。 实验显示,在正常行走条件下,我们的单模范方法在 CASIA-B 编程数据集中甚至达到96.1%的平均级/级精确度,甚至以87.9%的行走标精确度标码, 在复合方法下,它也达到了一种稳妥度的行进率。