Covariate Analysis for Footstep Recognition Using Unsupervised Hierarchical Clustering.

Footstep recognition is an emerging biometric that identifies or verifies users based on footstep pressure patterns obtained while walking. However, the impact of covariates on footstep recordings is not well understood, unlike more established biometric traits such as fingerprint and facial recognition. Therefore, this study used unsupervised hierarchical clustering (HCA) to examine the internal and external covariate influence on spatial and temporal footstep features of twenty individuals. Using 22 cluster validity indices, a robust HCA technique identified two distinct clusters in spatial representations (i.e., peak pressure images) and temporal representations (i.e., ground reaction force (GRF) and center of pressure (COP) time series) of the gait patterns. The clusters determined in both feature domains were distinguishable by body weight, age, race, and shoe type. Interestingly, trends related to sex and walking speed existed only in the temporal domain. These findings suggest dual implications for footstep biometric systems, which may leverage covariate information as soft biometrics to improve user recognition, or require mitigation to limit model bias and improve generalization to new users and conditions.