Enhancing gait recognition: data augmentation via physics-based biomechanical simulation

This paper focuses on addressing the problem of data scarcity for gait analysis. Standard augmentation methods may produce gait sequences that may not be consistent with the biomechanical constraints of human walking. To address this issue, we propose a novel framework for gait data augmentation by using physics-based simulation to synthesize biomechanically plausible walking sequences. The proposed approach is validated by augmenting the WBDS and CASIA-B datasets and then training gait-based classifiers for 3D gender gait classification and 2D gait person identification respectively. Experimental results indicate that our augmentation approach improves the performance of model-based gait classifiers and outperforms previous gait-based person identification methods, achieving an accuracy of up to 96.11% on the CASIA-B dataset.