Abstract
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.
Abstract (translated)
步识别(GR)是一种正在增长的生物特征识别方式,通过视觉摄像机用于远距离人员身份识别。GR提供了指纹和面部识别的可靠和安全替代品,因为更难区分虚假和真实信号。此外,它的抗伪造能力使GR适用于各种环境。随着深度学习的兴起,GR技术稳步前进,在各种情况下取得了令人瞩目的成果。随着视频监控越来越普遍,出现了新的问题,例如确保不同协议下一致的性能评估、即使在不同照明条件下也能可靠识别、步态模式的不规则变化以及保护隐私。本调查旨在提供一个概述GR的情况,并分析与环境元素和复杂性相比可能对其产生影响的其他生物特征识别系统。其主要目标是审查现有的人类步识别(GR)技术,可能为新的研究机会提供支持。
URL
https://arxiv.org/abs/2309.10144
