  Gait recognition is a promising biometric technology for identification due
to its non-invasiveness and long-distance. However, external variations such as
clothing changes and viewpoint differences pose significant challenges to gait
recognition. Silhouette-based methods preserve body shape but neglect internal
structure information, while skeleton-based methods preserve structure
information but omit appearance. To fully exploit the complementary nature of
the two modalities, a novel triple branch gait recognition framework, TriGait,
is proposed in this paper. It effectively integrates features from the skeleton
and silhouette data in a hybrid fusion manner, including a two-stream network
to extract static and motion features from appearance, a simple yet effective
module named JSA-TC to capture dependencies between all joints, and a third
branch for cross-modal learning by aligning and fusing low-level features of
two modalities. Experimental results demonstrate the superiority and
effectiveness of TriGait for gait recognition. The proposed method achieves a
mean rank-1 accuracy of 96.0% over all conditions on CASIA-B dataset and 94.3%
accuracy for CL, significantly outperforming all the state-of-the-art methods.
The source code will be available at https://github.com/feng-xueling/TriGait/.
