GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
This work addresses the lack of temporal information in gait recognition, an incremental improvement for biometric identification systems.
The paper tackles the problem of gait recognition by incorporating temporal features at varying granularities and spans, achieving state-of-the-art performance with Rank-1 accuracies of up to 98.2% on CASIA-B and 97.6% on OU-MVLP datasets.
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.