  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.
