A Gait Recognition Method via Graph Convolutional Networks With Local Mask

ABSTRACT To overcome the limitation of uniform graph convolution in skeleton‐based gait recognition—specifically its inability to adaptively model coordinated limb swings—this paper proposes a novel framework named MA‐Gait. The core innovation is a local masking mechanism integrated with a global context mask, applied to key body parts including arms, legs, head, and trunk. This guides a multi‐head attention graph convolutional network to enhance feature extraction from these locally masked regions during coordinated movements. Additionally, multi‐semantic gait data, comprising centripetal relative joint coordinates and skeletal lengths, are constructed to enrich gait representation through a multibranch architecture for parallel feature learning. Evaluated on CASIA‐B and OUMVLP‐Pose datasets, MA‐Gait achieves average recognition accuracies of 91.4% and 62.2%, respectively, significantly outperforming existing model‐based methods. The results validate the effectiveness of the local masking mechanism, multibranch design, and multi‐semantic learning paradigm in capturing discriminative gait features under complex conditions such as viewpoint changes and occlusion.