Joint Gait-Cadence Analysis for Human Identification Using an Elastic Shape Framework

Abstract

We perform human identification by gait recognition where subjects' gait is represented by silhouettes which are elements of a manifold of Square-Root Velocity functions. Gait cycles become stochastic processes on this manifold; cadence its rate of execution. Using geometry of this manifold, we compute mean gait cycle templates for subjects. An observation model, where test sequences are random perturbations of templates, produces likelihood functions for classification. We perform temporal registration—linear and nonlinear—of cycles with templates, removing cadence effects. In an experiment on 26 individuals, linear registration, preserving cadence, performs better than nonlinear registration, which removes cadence.