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

Abstract

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