  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.
