Robust gait-based person identification against walking speed variations

Abstract

Variations in walking speed have a strong impact on gaitbased person identification. We propose a method that is robust against walking-speed variations. It is based on a combination of cubic higherorder local auto-correlation (CHLAC), gait silhouette-based principal component analysis (GSP), and a statistical framework using hidden Markov models (HMMs). The CHLAC features capture the within-phase spatiotemporal characteristics of each individual, the GSP features retain more shape/phase information for better gait sequence alignment, and the HMMs classify the ID of each gait even when walking speed changes nonlinearly. We compared the performance of our method with other conventional methods using five different databases, SOTON, USF-NIST, CMU-MoBo, TokyoTech A and TokyoTech B. The proposed method was equal to or better than the others when the speed did not change greatly, and it was significantly better when the speed varied across and within a gait sequence. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.