Paper Info
Title
Robust Gait Recognition Against Speed Variation
Abstract
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.
Year
DOI
Venue
2010
10.1109/ICPR.2010.536
Pattern Recognition
Keywords
Field
DocType
gait analysis,hidden Markov models,image motion analysis,CHLAC,FDA,Fisher discriminant analysis,HMM,cubic higher order local auto correlation,hidden Markov models,robust gait recognition,spatio temporal characteristics,speed variation,statistical framework,walking speed variations
Gait,Computer science,Robustness (computer science),Gait analysis,Artificial intelligence,Computer vision,Pattern recognition,Speech recognition,Feature extraction,Linear discriminant analysis,Hidden Markov model,Principal component analysis,Preferred walking speed
Conference
ISSN
ISBN
Citations 
1051-4651
978-1-4244-7542-1
11
PageRank 
References 
Authors
0.57
8
3
Name
Order
Citations
PageRank
Muhammad Rasyid Aqmar1261.83
Koichi Shinoda246365.14
Sadaoki Furui31639216.96