GaitBranch: A multi-branch refinement model combined with frame-channel attention mechanism for gait recognition

Accurately representing human motion in video-based gait recognition is challenging due to the difficulty in obtaining an ideal gait silhouette sequence that captures comprehensive information. To address this challenge, we propose GaitBranch, a novel method that emphasizes local key information of human motion in different layers of the neural network. It divides the neural network into multiple branches using the multi-branch refinement (MBR) module and extracts local key frames from various body parts through the frame-channel attention mechanism (FCAM) to form a comprehensive representation of human motion patterns. GaitBranch achieves high gait recognition accuracy on the CASIA-B (98.6%, 96.1%, and 85.5% for normal walking, carrying a bag, and wearing a coat conditions), OU-MVLP (92.3%), and GREW (79.8%) datasets, demonstrating its robustness across different environments. Ablation experiments confirm the efficacy of our method and demonstrate that the performance gains result from the optimized model structure rather than simply increasing parameters.