Visual surveillance has exponentially increased the growth of security devices and systems in the digital era. Gait-based person identification is an emerging biometric modality for automatic visual surveillance and monitoring as the walking patterns highly correlate to the subject’s identity. The scientific research on person identification using gait has grown dramatically over the past two decades due to its several benefits. It does not require active collaboration from users and can be performed without their cooperation. It is difficult to be impersonated and identification can be validated from low-resolution videos and with simple instrumentation. This paper presents a comprehensive overview of the exiting techniques, their key stages, and recent developments in vision-based person identification using gait. We reviewed the historical research on gait locomotion and explain that how it is used to recognize the identity. The article summarizes the different types of features that have been proposed to encode the biomechanics of gait and also groups them into different categories and subcategories based upon the similarity in their implementation. We also present the impact of different covariate factors that affect the performance of gait recognition systems and also discuss the recent works to cope with these challenges. Furthermore, a comparison of the recognition accuracies reported by the existing algorithms to assess their performance under verification and identification mode is also presented. A detailed summary of publicly available vision-based gait databases is also provided. Finally, it offers insight into the challenges and open problems for future perspectives in the field of gait recognition that can help to set the directions for future research in this field.

在数字时代，视觉监控成倍增加了安全设备和系统的增长。基于步态的人员识别是一种新兴的生物识别方式，用于自动视觉监视和监测，因为步行模式与主体的身份高度相关。在过去的 20 年里，由于步态的几个好处，关于使用步态进行个人识别的科学研究急剧增长。它不需要用户的积极协作，并且可以在没有他们合作的情况下执行。很难被模仿，并且可以从低分辨率视频和简单的仪器中验证身份。本文全面概述了现有技术、它们的关键阶段以及使用步态进行基于视觉的人员识别的最新发展。我们回顾了步态运动的历史研究，并解释了如何使用它来识别身份。本文总结了已提出的用于编码步态生物力学的不同类型的特征，并根据其实现的相似性将它们分为不同的类别和子类别。我们还介绍了影响步态识别系统性能的不同协变量因素的影响，并讨论了最近应对这些挑战的工作。此外，还比较了现有算法报告的识别精度，以评估其在验证和识别模式下的性能。还提供了公开可用的基于视觉的步态数据库的详细摘要。最后，