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

Hadi Sadoghi Yazdi | Zendy; Hessam Jahani Fariman | Zendy; Jaber Roohi | Zendy

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

Author(s) -

Hadi Sadoghi Yazdi,

Hessam Jahani Fariman,

Jaber Roohi

Publication year - 2011

Publication title -

isrn artificial intelligence

Language(s) - English

Resource type - Journals

eISSN - 2090-7443

pISSN - 2090-7435

DOI - 10.5402/2012/289721

Subject(s) - gait , artificial intelligence , computer science , pattern recognition (psychology) , classifier (uml) , fuzzy logic , invariant (physics) , benchmarking , gait analysis , algorithm , computer vision , mathematics , physical medicine and rehabilitation , medicine , marketing , business , mathematical physics

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.

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