As an important biometric technology, gait recognition methods are used to identify individuals based on their unique walking patterns. Empirically, different local regions of a pedestrian contribute variably to identification due to variations in walking conditions. Besides, the appearance of silhouette frames tends to be imperfect and variable, as segmentation errors frequently occur during pedestrian segmentation and pedestrians present different postures in walking. From a perspective of intuition, it is pivotal to measure the spatial importance of each local body region and the temporal importance of each frame during the gait representation learning. Based on this analysis, we heuristically propose Importance-Aware Spatial-Temporal representation Learning (IAGait), a novel gait recognition framework which utilize spatial and temporal importance to aggregate discriminative gait representations, assessing the contributions of local spatial regions and different temporal frames. IAGait is composed of two main modules: the Spatial-Importance-Aware Module (SIAM) which focuses on learning the importance of spatial horizontal parts, and the Temporal-Importance-Aware Module (TIAM) which learns temporal importance using the self-attention mechanism. In detail, SIAM introduces a weak supervision strategy for the spatial importance learning and Pseudo-Label Assignment (PLA) for part importance with metric learning hardness. And TIAM adopts a View-Information-Erasing Module (VIEM) to eliminate view information and obtain view-invariant representations, since the view variation can disrupt temporal importance awareness. Extensive experiments demonstrate the effectiveness of the proposed modules, with our method achieving state-of-the-art performance on popular datasets. We plan to release the source code for further research and development.

Existing methods combining skeleton and silhouette representations demonstrate explicit effectiveness for gait recognition. However, current related methods simply combine the video-level representations of model-based skeleton data and gait silhouettes for retrieval. Therefore, diverse skeleton information is not fully exploited in existing related works: Firstly, the position and movement of bones are not clear from individual silhouettes. This indicates that the frame-level interaction between features of skeletons and silhouettes is critical, which is ignored by previous methods. Secondly, diverse part-level skeleton-guided gait features are not fully captured in existing related approaches. To solve the above issues, we present a novel framework with multi-level skeleton-guided refinement, including frame-level, part-level, and video-level skeleton-guided refinement, for comprehensive skeleton-aided gait representation learning. First, two modules are proposed for frame-level skeleton-guided refinement. Specifically, Visual Skeleton Enhanced Backbone (VSEB) is proposed to visually highlight the global and part-level skeleton regions for the feature of each silhouette frame. Moreover, Cross-Visual-Model Frame-level Interaction (CVMFI) is proposed to further transfer the model-based skeleton information to features of the visual modalities. Secondly, part-level visual and model-based skeleton features are utilized to refine the final gait representation. Concretely, in VSEB, Part Skeleton Enhance Network (PSEN) is proposed to visually enhance the position and movement of part-level skeletons. In addition, Semantic Part Pooling (SPP) is proposed for capturing the model-based skeleton features of different semantic parts. Finally, as the video-level skeleton-guided refinement, multimodal video-level features are combined to boost the final recognition performance. Extensive experimental results on prevailing datasets demonstrate that our approach outperforms most existing methods, including the skeleton-aided multi-modal methods. With the multi-level refinement guided by the skeleton modalities, the framework is expected to provide a deeper understanding of skeleton-aided gait recognition.