  Gait, an unobtrusive biometric, is valued for its capability to identify
individuals at a distance, across external outfits and environmental
conditions. This study challenges the prevailing assumption that vision-based
gait recognition, in particular skeleton-based gait recognition, relies
primarily on motion patterns, revealing a significant role of the implicit
anthropometric information encoded in the walking sequence. We show through a
comparative analysis that removing height information leads to notable
performance degradation across three models and two benchmarks (CASIA-B and
GREW). Furthermore, we propose a spatial transformer model processing
individual poses, disregarding any temporal information, which achieves
unreasonably good accuracy, emphasizing the bias towards appearance information
and indicating spurious correlations in existing benchmarks. These findings
underscore the need for a nuanced understanding of the interplay between motion
and appearance in vision-based gait recognition, prompting a reevaluation of
the methodological assumptions in this field. Our experiments indicate that
"in-the-wild" datasets are less prone to spurious correlations, prompting the
need for more diverse and large scale datasets for advancing the field.
