  The analysis of patterns of walking is an important area of research that has
numerous applications in security, healthcare, sports and human-computer
interaction. Lately, walking patterns have been regarded as a unique
fingerprinting method for automatic person identification at a distance. In
this work, we propose a novel gait recognition architecture called Gait Pyramid
Transformer (GaitPT) that leverages pose estimation skeletons to capture unique
walking patterns, without relying on appearance information. GaitPT adopts a
hierarchical transformer architecture that effectively extracts both spatial
and temporal features of movement in an anatomically consistent manner, guided
by the structure of the human skeleton. Our results show that GaitPT achieves
state-of-the-art performance compared to other skeleton-based gait recognition
works, in both controlled and in-the-wild scenarios. GaitPT obtains 82.6%
average accuracy on CASIA-B, surpassing other works by a margin of 6%.
Moreover, it obtains 52.16% Rank-1 accuracy on GREW, outperforming both
skeleton-based and appearance-based approaches.
