Robust Gait Recognition Against Speed Variation

Variations in walking speed have a strong impact on the recognition of gait. We propose a method of recognition of gait that is robust against walking-speed variations. It is established on a combination of Fisher discriminant analysis (FDA)-based cubic higher-order local auto-correlation (CHLAC) and the statistical framework provided by hidden Markov models (HMMs). The HMMs in this method identify the phase of each gait even when walking speed changes nonlinearly, and the CHLAC features capture the within-phase spatio-temporal characteristics of each individual. We compared the performance of our method with other conventional methods in our evaluation using three different databases, i.e., USH, USF-NIST, and Tokyo Tech DB. Ours was equal or better than the others when the speed did not change too much, and was significantly better when the speed varied across and within a gait sequence.