  Gait recognition captures gait patterns from the walking sequence of an
individual for identification. Most existing gait recognition methods learn
features from silhouettes or skeletons for the robustness to clothing,
carrying, and other exterior factors. The combination of the two data
modalities, however, is not fully exploited. Previous multimodal gait
recognition methods mainly employ the skeleton to assist the local feature
extraction where the intrinsic discrimination of the skeleton data is ignored.
This paper proposes a simple yet effective Bimodal Fusion (BiFusion) network
which mines discriminative gait patterns in skeletons and integrates with
silhouette representations to learn rich features for identification.
Particularly, the inherent hierarchical semantics of body joints in a skeleton
is leveraged to design a novel Multi-Scale Gait Graph (MSGG) network for the
feature extraction of skeletons. Extensive experiments on CASIA-B and OUMVLP
demonstrate both the superiority of the proposed MSGG network in modeling
skeletons and the effectiveness of the bimodal fusion for gait recognition.
Under the most challenging condition of walking in different clothes on
CASIA-B, our method achieves the rank-1 accuracy of 92.1%.
