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/.

翻译：步态识别因其非侵入性和远距离特性而成为一种有前景的生物识别技术。然而，衣物变换和视角差异等外部变化对步态识别构成了显著挑战。基于轮廓的方法保留了身体形状，但忽略了内部结构信息，而基于骨架的方法保留了结构信息，但忽略了外观。为了充分利用这两种模态的互补特性，本文提出了一种新颖的三分支步态识别框架TriGait。该框架以混合融合方式有效整合了骨架与轮廓数据的特征，包括一个用于从外观中提取静态和运动特征的双流网络、一个名为JSA-TC的简单有效模块以捕获所有关节间的依赖关系，以及第三个用于通过对齐与融合两种模态的低级特征进行跨模态学习的分支。实验结果证明了TriGait在步态识别上的优越性和有效性。该方法在CASIA-B数据集上所有条件下的平均Rank-1准确率达到96.0%，在CL条件下准确率为94.3%，显著优于所有现有最先进方法。源代码将发布于https://github.com/feng-xueling/TriGait/。