  Gait recognition is a promising biometric method that aims to identify
pedestrians from their unique walking patterns. Silhouette modality, renowned
for its easy acquisition, simple structure, sparse representation, and
convenient modeling, has been widely employed in controlled in-the-lab
research. However, as gait recognition rapidly advances from in-the-lab to
in-the-wild scenarios, various conditions raise significant challenges for
silhouette modality, including 1) unidentifiable low-quality silhouettes
(abnormal segmentation, severe occlusion, or even non-human shape), and 2)
identifiable but challenging silhouettes (background noise, non-standard
posture, slight occlusion). To address these challenges, we revisit gait
recognition pipeline and approach gait recognition from a quality perspective,
namely QAGait. Specifically, we propose a series of cost-effective quality
assessment strategies, including Maxmial Connect Area and Template Match to
eliminate background noises and unidentifiable silhouettes, Alignment strategy
to handle non-standard postures. We also propose two quality-aware loss
functions to integrate silhouette quality into optimization within the
embedding space. Extensive experiments demonstrate our QAGait can guarantee
both gait reliability and performance enhancement. Furthermore, our quality
assessment strategies can seamlessly integrate with existing gait datasets,
showcasing our superiority. Code is available at
https://github.com/wzb-bupt/QAGait.
