Hybridization of discrete binary particle swarm optimization and invariant moments for dorsal hand vein feature selection

Asmaa Merouane, S. Benziane, Pierre Boulet, Abou El Hassan Benyamina, Lakhdar Loukil
{"title":"Hybridization of discrete binary particle swarm optimization and invariant moments for dorsal hand vein feature selection","authors":"Asmaa Merouane, S. Benziane, Pierre Boulet, Abou El Hassan Benyamina, Lakhdar Loukil","doi":"10.1109/ECAI.2013.6636192","DOIUrl":null,"url":null,"abstract":"Nowadays, hand vein recognition is amongst the most recent biometric technologies used for the identification/authentication of individuals. Indeed, hand veins biometric are robust and steady human authentication unlike to other biometric technologies as fingerprint, face, signature and voice. In the present work, the proposed system consists of image preprocessing, feature extraction and identification. This paper outlines a novel approach for identification, based on seven Hu's invariant moments that are extracted from the vein images as feature representation, due to its invariant features on image translation, scaling and rotation. However, they are sensitive to noise. Therefore, discrete binary particle swarm optimization (PSO) is applied in solving a problem of optimization; for selecting optimal features of Hu's invariant moments that minimize false accept rate (FAR) and false reject rate (FRR).The experimental results carried out on 102 users show that the discrete binary PSO-Invariant Moments improve the performance of our biometric system with FAR= 0% and FRR=0%, with fewer number of features and threshold of 72%.","PeriodicalId":105698,"journal":{"name":"Proceedings of the International Conference on ELECTRONICS, COMPUTERS and ARTIFICIAL INTELLIGENCE - ECAI-2013","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on ELECTRONICS, COMPUTERS and ARTIFICIAL INTELLIGENCE - ECAI-2013","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECAI.2013.6636192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Nowadays, hand vein recognition is amongst the most recent biometric technologies used for the identification/authentication of individuals. Indeed, hand veins biometric are robust and steady human authentication unlike to other biometric technologies as fingerprint, face, signature and voice. In the present work, the proposed system consists of image preprocessing, feature extraction and identification. This paper outlines a novel approach for identification, based on seven Hu's invariant moments that are extracted from the vein images as feature representation, due to its invariant features on image translation, scaling and rotation. However, they are sensitive to noise. Therefore, discrete binary particle swarm optimization (PSO) is applied in solving a problem of optimization; for selecting optimal features of Hu's invariant moments that minimize false accept rate (FAR) and false reject rate (FRR).The experimental results carried out on 102 users show that the discrete binary PSO-Invariant Moments improve the performance of our biometric system with FAR= 0% and FRR=0%, with fewer number of features and threshold of 72%.
离散二元粒子群优化与不变矩杂交手背静脉特征选择
如今,手部静脉识别是用于个人身份识别/认证的最新生物识别技术之一。事实上,与指纹、面部、签名和声音等其他生物识别技术不同,手静脉生物识别技术是一种鲁棒且稳定的人类身份验证技术。在本工作中,提出的系统包括图像预处理、特征提取和识别。本文提出了一种新的识别方法,该方法基于从静脉图像中提取的7个胡氏不变矩作为特征表示,因为它具有图像平移、缩放和旋转的不变特征。然而,它们对噪音很敏感。因此,将离散二元粒子群算法(PSO)应用于求解优化问题;选取Hu不变矩的最优特征,使误接受率(FAR)和误拒绝率(FRR)最小。102个用户的实验结果表明,在FAR= 0%、FRR=0%、特征数量较少、阈值为72%的情况下,离散二值pso不变矩提高了生物识别系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信