Model Based Gait Recognition

Model-based gait recognition aims to identify individuals based on their walking patterns using skeletal data, offering robustness against clothing and carrying variations that affect appearance-based methods. Current research heavily utilizes graph convolutional networks (GCNs) and other deep learning architectures, often incorporating techniques like diffusion models to integrate skeletal and silhouette information for improved accuracy. This approach is significant because it leverages the inherent 3D structure of gait, leading to more reliable identification, particularly in challenging real-world scenarios and potentially impacting applications like security and surveillance.