Gait recognition (GR) is a growing biometric modality used for person identification from a distance through visual cameras. GR provides a secure and reliable alternative to fingerprint and face recognition, as it is harder to distinguish between false and authentic signals. Furthermore, its resistance to spoofing makes GR suitable for all types of environments. With the rise of deep learning, steadily improving strides have been made in GR technology with promising results in various contexts. As video surveillance becomes more prevalent, new obstacles arise, such as ensuring uniform performance evaluation across different protocols, reliable recognition despite shifting lighting conditions, fluctuations in gait patterns, and protecting privacy.This survey aims to give an overview of GR and analyze the environmental elements and complications that could affect it in comparison to other biometric recognition systems. The primary goal is to examine the existing deep learning (DL) techniques employed for human GR that may generate new research opportunities.

该研究综述了步态识别技术的发展和应用，并分析了环境因素和复杂性，比较了它与其他生物识别系统的差异，同时探讨了深度学习技术在人类步态识别中的应用和可能的研究机会。

基于深度学习的人体步态识别：综述