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Gait recognition is an emerging biological recognition technology thatidentifies and verifies individuals based on their walking patterns. However,many current methods are limited in their use of temporal information. In orderto fully harness the potential of gait recognition, it is crucial to considertemporal features at various granularities and spans. Hence, in this paper, wepropose a novel framework named GaitGS, which aggregates temporal features inthe granularity dimension and span dimension simultaneously. Specifically,Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturingthe micro-motion and macro-motion information at the frame level and unit levelrespectively. Moreover, we present Multi-Span Feature Learning (MSFL) module togenerate global and local temporal representations. On three popular gaitdatasets, extensive experiments demonstrate the state-of-the-art performance ofour method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0%(+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. Thesource code will be released soon.

GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition