Gait recognition, a growing field in biological recognition technology, utilizes distinct walking patterns for accurate individual identification. However, existing methods lack the incorporation of temporal information. To reach the full potential of gait recognition, we advocate for the consideration of temporal features at varying granularities and spans. This paper introduces a novel framework, GaitGS, which aggregates temporal features simultaneously in both granularity and span dimensions. Specifically, the Multi-Granularity Feature Extractor (MGFE) is designed to capture micro-motion and macro-motion information at fine and coarse levels respectively, while the Multi-Span Feature Extractor (MSFE) generates local and global temporal representations. Through extensive experiments on two datasets, our method demonstrates state-of-the-art performance, achieving Rank-1 accuracy of 98.2%, 96.5%, and 89.7% on CASIA-B under different conditions, and 97.6% on OU-MVLP. The source code will be available at https://github.com/Haijun-Xiong/GaitGS.

翻译：步态识别作为生物识别技术中一个不断发展的领域，利用个体独特的行走模式实现精确身份识别。然而，现有方法普遍缺乏对时序信息的有效融合。为充分挖掘步态识别的潜力，本文主张从不同粒度和跨度两个维度对时序特征进行建模。本文提出了一种新颖的框架GaitGS，该框架能够同时在粒度维度和跨度维度上聚合时序特征。具体而言，多粒度特征提取器（MGFE）被设计用于分别在细粒度和粗粒度层次捕获微观运动与宏观运动信息，而多跨度特征提取器（MSFE）则生成局部与全局的时序表征。通过在两个数据集上的大量实验，本方法展现了最先进的性能：在CASIA-B数据集的不同条件下分别达到了98.2%、96.5%和89.7%的Rank-1准确率，在OU-MVLP数据集上达到了97.6%的Rank-1准确率。源代码将在https://github.com/Haijun-Xiong/GaitGS 公开。