Table of Contents
ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 289721, 9 pages
Research Article

Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

Computer Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran

Received 26 July 2011; Accepted 28 August 2011

Academic Editor: M. Arif

Copyright © 2012 Hadi Sadoghi Yazdi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a human gait recognition algorithm based on a leg gesture separation. Main innovation in this paper is gait recognition using leg gesture classification which is invariant to covariate conditions during walking sequence and just focuses on underbody motions and a neuro-fuzzy combiner classifier (NFCC) which derives a high precision recognition system. At the end, performance of the proposed algorithm has been validated by using the HumanID Gait Challenge data set (HGCD), the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time. And it has been compared to recent algorithm of gait recognition.