Research on Gait Recognition Based on GaitSet and Multimodal Fusion

With the continuous technological progress, especially the development in biometrics, gait recognition has shown broad application prospects in healthcare (e.g., health monitoring), security (e.g., assisted identity verification), and human-computer interaction. However, individual differences, such as changes in physical condition, and environmental variability, such as differences in lighting, can impact its accuracy. Based on the information derived from the gait contour sequence during walking (such as temporal and spatial information), this study proposes an improved gait recognition method based on the GaitSet model, which improves video-based gait recognition performance by combining gait energy images and silhouette images to form a multimodal representation. The experimental results showed a significant performance improvement compared with the original model, especially in subjects with bags. Large-sample training experiment results based on the CASIA-B database indicated that the recognition rates in the Normal (NM), Bag (BG), and Coat (CL) states were 95.8%, 89.3%, and 72.5%, respectively, and that in the CL state achieved a significant improvement of 3.3%.