An efficient gait recognition based on a selective neural network ensemble

Authors:
Heesung Lee;Sungjun Hong;Euntai Kim
Affiliations:
School of Electrical and Electronic Engineering, Yonsei University, Seodaemun-Gu, Seoul 120-749, Korea;School of Electrical and Electronic Engineering, Yonsei University, Seodaemun-Gu, Seoul 120-749, Korea;School of Electrical and Electronic Engineering, Yonsei University, Seodaemun-Gu, Seoul 120-749, Korea
Venue:
International Journal of Imaging Systems and Technology
Year:
2008

Citing 0
Cited 2

A new design method for linguistically understandable fuzzy classifier

FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Unsupervised clustering of people from 'skeleton' data

HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).