Gait Recognition

Gait recognition, the identification of individuals based on their walking patterns, aims to develop robust and accurate systems for remote, non-intrusive person identification. Current research heavily focuses on improving recognition accuracy in challenging real-world conditions (e.g., varying viewpoints, occlusions, clothing changes) using diverse data modalities (RGB video, LiDAR point clouds, skeleton data) and advanced architectures like convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs). These advancements hold significant potential for applications in security, surveillance, healthcare (e.g., monitoring gait disorders), and human-computer interaction, particularly as researchers address the challenges of cross-modality fusion and data scarcity.