  Gait recognition aims to identify a person based on their walking sequences,
serving as a useful biometric modality as it can be observed from long
distances without requiring cooperation from the subject. In representing a
person's walking sequence, silhouettes and skeletons are the two primary
modalities used. Silhouette sequences lack detailed part information when
overlapping occurs between different body segments and are affected by carried
objects and clothing. Skeletons, comprising joints and bones connecting the
joints, provide more accurate part information for different segments; however,
they are sensitive to occlusions and low-quality images, causing
inconsistencies in frame-wise results within a sequence. In this paper, we
explore the use of a two-stream representation of skeletons for gait
recognition, alongside silhouettes. By fusing the combined data of silhouettes
and skeletons, we refine the two-stream skeletons, joints, and bones through
self-correction in graph convolution, along with cross-modal correction with
temporal consistency from silhouettes. We demonstrate that with refined
skeletons, the performance of the gait recognition model can achieve further
improvement on public gait recognition datasets compared with state-of-the-art
methods without extra annotations.
