A gait recognition algorithm integrating spatiotemporal multi-scale and local fine-grained features

In response to the issue of the GaitSet algorithm's backbone network being ineffective in feature learning and easily ignoring fine-grained features in local areas, we propose a gait recognition algorithm based on multi-scale fine-grained feature fusion (MSFG-Gaitset). This algorithm reconstructs the GaitSet network by introducing a multi-scale feature extraction module and a local feature extraction module, which allows for the extraction of multi-scale features while dividing the feature map into multiple parts for fine-grained feature extraction. Additionally, we improve the hard-batch triplet loss function to optimize the gait model, making the training process more effective. To further enhance the model’s performance, features at different layers are fused to obtain richer feature representations. To validate the algorithm’s effectiveness, we tested it on the CASIA-B dataset, and the results showed that the gait recognition accuracy reached 85.81%, a 2.6% improvement over GaitSet. This demonstrates that the MSFG-GaitSet algorithm can effectively mitigate the negative impact of complex environments on gait recognition performance and achieve high-accuracy gait recognition in complex scenarios. Experimental data fully demonstrate that this method not only has high gait recognition accuracy but also exhibits good robustness and wide applicability.