MCDGait: multimodal co-learning distillation network with spatial-temporal graph reasoning for gait recognition in the wild

Gait recognition in the wild has attracted the attention of the academic community. However, existing unimodal algorithms cannot achieve the same performance on in-the-wild datasets as in-the-lab datasets because unimodal data have many limitations in-the-wild environments. Therefore, we propose a multimodal approach combining silhouettes and skeletons and formulate the multimodal gait recognition problem as a multimodal co-learning problem. In particular, we propose a multimodal co-learning distillation network (MCDGait) that integrates two sub-networks processing unimodal data into a single fusion network. Based on the semantic consistency of different modalities and the paradigm of deep mutual learning, the performance of the entire network is continuously improved via the bidirectional knowledge distillation between the sub-networks and fusion network. Inspired by the observation that specific body parts or joints exhibit unique motion characteristics and have linkage with other parts or joints during walking, we propose a spatial-temporal graph reasoning module (ST-GRM). This module represents the parts or joints as graph nodes and the motion linkages between them as edges. By utilizing dynamic graph generator, the module implicitly captures the dynamic changes of the human body. Based on the generated graphs, the independent spatial-temporal linkage feature of each part and the interactive spatial-temporal linkage feature are aggregated simultaneously. Extensive experiments conducted on two in-the-wild datasets demonstrate the state-of-the-art performance of the proposed method. The average rank-1 accuracy on datasets Gait3D and GREW is 50.90% and 58.06%, respectively. The source code can be obtained from https://github.com/BoyeXiong/MCDGait.