NDLI: Gait recognition method based on hybrid kernel and optimized parameter SVM

Content Provider	IEEE Xplore Digital Library
Author	Jian Ni Libo Liang
Copyright Year	2009
Description	Author affiliation: College of information and Electronic Engineering, Hebei University of Engineering, Handan, China, 056038 (Jian Ni; Libo Liang)
Abstract	The gait recognition algorithm adopt support vector machine based on hybrid kernel function and Parameter Optimization. Partial kernel function and overall kernel function are fitted to compose super-kernel function, so that the SVM obtain better generalization ability and generalization ability. In terms of parameter selection, the text uses the objective function and combine OPS algorithm to select the best kernel parameter. This method makes use of the distance of training samples of different classes to find the optimal (or effective) nuclear parameters instead of the standard SVM training samples. It avoids strong empirical and large amount of calculation of the traditional SVM on model selection. Then the gaits are classified by the support vector machine models. This algorithm is applied to a data-set including thirty individuals. Experimental results demonstrate that the algorithm performs at an encouraging recognition rate and at a relatively lower computational cost.
Starting Page	60
Ending Page	63
File Size	157907
Page Count	4
File Format	PDF
ISBN	9781424445196
DOI	10.1109/ICCSIT.2009.5234612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition gait recognition Optimization methods PSO algorithm Educational institutions Image sequences SVM Data mining Support vector machines Support vector machine classification Feature extraction Computational efficiency Kernel hybrid kernel objective function
Content Type	Text
Resource Type	Article

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