High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29120

Title:	High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier
Authors:	Chen,CH;Chu,CT
Contributors:	資訊工程研究所
Date:	2009
Issue Date:	2010-06-29 20:14:12 (UTC+8)
Publisher:	中央大學
Abstract:	In this paper, a novel iris feature extraction technique with intelligent classifier is proposed for high performance iris recognition. We use one dimensional circular profile to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1-D wavelet transform. So as to improve the accuracy, this paper combines probabilistic neural network (PNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition. (C) 2009 Published by Elsevier Ltd.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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