HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition
arXiv:2204.10238 · doi:10.1109/CTISC54888.2022.9849799
Abstract
Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmentation techniques, we propose a powerful feature extractor that utilizes ResGCN to achieve state-of-the-art performance in model-based gait recognition on the CASIA-B gait dataset.
Accepted in 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC 2022). 6 pages, 4 figures, 2 tables