Sepas-Moghaddam Alireza, Etemad Ali
IEEE Trans Pattern Anal Mach Intell. 2023 Jan;45(1):264-284. doi: 10.1109/TPAMI.2022.3151865. Epub 2022 Dec 5.
Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.
步态识别是一种很有吸引力的生物识别方式，旨在根据个人的行走方式来识别他们。自2015年以来，深度学习通过自动学习判别性表征的能力重塑了该领域的研究格局。基于深度学习的步态识别方法目前在该领域占据了领先地位，并推动了实际应用的发展。在本文中，我们全面概述了深度学习在步态识别方面的突破和最新进展，并涵盖了广泛的主题，包括数据集、测试协议、先进解决方案、挑战以及未来的研究方向。我们首先回顾常用的步态数据集以及为评估它们而设计的原则。然后，我们提出了一种新颖的分类法，由四个独立的维度组成，即身体表征、时间表征、特征表征和神经架构，以帮助刻画和组织该领域的研究格局和文献。按照我们提出的分类法，对使用深度学习的步态识别方法进行了全面综述，并讨论了它们的性能、特点、优点和局限性。我们以对当前挑战的讨论结束本次综述，并提及了步态识别未来研究的一些有前景的方向。
