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