Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the clas...

Full description

Saved in:

Bibliographic Details
Published in:	Acta Informatica Pragensia Vol. 12; no. 1; pp. 3 - 18
Main Authors:	Balogun, Monsurat Omolara, Odeniyi, Latifat Adeola, Omidiora, Elijah Olusola, Olabiyisi, Stephen Olatunde, Falohun, Adeleye Samuel
Format:	Journal Article
Language:	English
Published:	Vysoká škola ekonomická v Praze 01.01.2023 Prague University of Economics and Business
Subjects:	artificial immune system ICT Information and Communications Technologies negative selection algorithm nsa optimized negative selection algorithm recognition accuracy teaching-learning-based optimization algorithm Recognition accuracy Optimized negative selection algorithm Teachinglearningbased optimization algorithm Negative selection algorithm NSA Artificial immune system
ISSN:	1805-4951, 1805-4951
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.
AbstractList	Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.
Author	Falohun, Adeleye Samuel Balogun, Monsurat Omolara Olabiyisi, Stephen Olatunde Omidiora, Elijah Olusola Odeniyi, Latifat Adeola
Author_xml	– sequence: 1 fullname: Balogun, Monsurat Omolara – sequence: 2 fullname: Odeniyi, Latifat Adeola – sequence: 3 fullname: Omidiora, Elijah Olusola – sequence: 4 fullname: Olabiyisi, Stephen Olatunde – sequence: 5 fullname: Falohun, Adeleye Samuel
BookMark	eNpNkE1LxDAQhoMo-HnxLuQsrHbaJE2OuvixoO5h9WqYppM1pW2WtAr66y2riKd5mHl5YN5DttvHnhg7hewCdK7Ky-YCw2ZitcMOQGdyJoyE3X-8z06GocmyDHIplZQH7HW5GUMXvqjmT7TGMXwQX1FLbgyx51ftOqYwvnXcx8QXHa6Jz1schuCDw20k9PzxvZ0cscaWX4fY0ZiC46vPYaTumO15bAc6-Z1H7OX25nl-P3tY3i3mVw8zV4AYZ3lRgQKZe1NSnWlXoqECfVFIqBxVFU4X7bQqdanBUO6lrypvlCs1utqL4ogtfrx1xMZuUugwfdqIwW4XMa0tpjG4lqwwVW00SMiEEL4yuhSQkyiRtFIezeQ6_3G5FIchkf_zQWa3RdvGTkVPrKbw2W-YKLa2ie-pnx61kAOoQhTfPbx9tg
Cites_doi	10.1007/978-3-540-89991-4_23 10.1162/jocn.1991.3.1.71 10.1155/2015/198363 10.1166/asl.2014.5458 10.4304/jcp.5.9.1348-1355 10.1016/j.ins.2011.08.006 10.1016/j.patcog.2007.06.035 10.1016/j.proeng.2011.11.2496 10.1007/s11432-011-4323-7 10.1109/TSP.2004.833862 10.48550/arXiv.1905.04717 10.1016/j.jksuci.2019.09.003 10.1016/j.dsp.2004.05.001 10.1109/ICCICCT.2014.6993023 10.1007/s41870-020-00501-0
ContentType	Journal Article
DBID	AE2 BIXPP REL AAYXX CITATION DOA
DOI	10.18267/j.aip.186
DatabaseName	Central and Eastern European Online Library (C.E.E.O.L.) (DFG Nationallizenzen) CEEOL: Open Access Central and Eastern European Online Library - CEEOL Journals CrossRef DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef
DatabaseTitleList
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
DocumentTitleAlternate	Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System
EISSN	1805-4951
EndPage	18
ExternalDocumentID	oai_doaj_org_article_49bd981510444fb987412e47ae866fa9 10_18267_j_aip_186 1211634
GroupedDBID	5VS ADBBV AE2 ALMA_UNASSIGNED_HOLDINGS BCNDV BIXPP GROUPED_DOAJ IPNFZ KQ8 M~E OK1 REL RIG AAYXX CITATION
ID	FETCH-LOGICAL-c314t-23b16152f97ed08c7a9e3af3351bcebba2f98c86787819e2f5fbbf96c78acdf43
IEDL.DBID	DOA
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001106204000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1805-4951
IngestDate	Fri Oct 03 12:28:38 EDT 2025 Sat Oct 25 04:57:35 EDT 2025 Tue Oct 28 18:51:40 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Recognition accuracy Optimized negative selection algorithm Teachinglearningbased optimization algorithm Negative selection algorithm NSA Artificial immune system
Language	English
License	https://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c314t-23b16152f97ed08c7a9e3af3351bcebba2f98c86787819e2f5fbbf96c78acdf43
ORCID	0000-0003-3666-4282
OpenAccessLink	https://doaj.org/article/49bd981510444fb987412e47ae866fa9
PageCount	16
ParticipantIDs	ceeol_journals_1211634 crossref_primary_10_18267_j_aip_186 doaj_primary_oai_doaj_org_article_49bd981510444fb987412e47ae866fa9
PublicationCentury	2000
PublicationDate	2023-01-01
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– month: 01 year: 2023 text: 2023-01-01 day: 01
PublicationDecade	2020
PublicationSubtitle	Acta Informatica Pragensia
PublicationTitle	Acta Informatica Pragensia
PublicationTitleAlternate	Acta Informatica Pragensia
PublicationYear	2023
Publisher	Vysoká škola ekonomická v Praze Prague University of Economics and Business
Publisher_xml	– name: Prague University of Economics and Business – name: Vysoká škola ekonomická v Praze
References	ref13 Sanches (ref12) 2007 ref14 Thakur (ref15) 2017; 4 ref11 ref10 ref0 ref2 ref1 Chikh (ref4) 2017; 6 ref17 ref16 Falohun (ref5) 2013 ref18 ref8 ref7 ref9 ref3 ref6
References_xml	– ident: ref7 doi: 10.1007/978-3-540-89991-4_23 – ident: ref17 doi: 10.1162/jocn.1991.3.1.71 – ident: ref6 doi: 10.1155/2015/198363 – volume: 4 start-page: 1588 issue: 11 year: 2017 ident: ref15 article-title: A Review of Image Classification Techniques. International Research publication-title: Journal of Engineering and Technology – ident: ref14 doi: 10.1166/asl.2014.5458 – ident: ref8 doi: 10.4304/jcp.5.9.1348-1355 – ident: ref11 doi: 10.1016/j.ins.2011.08.006 – ident: ref18 doi: 10.1016/j.patcog.2007.06.035 – ident: ref0 doi: 10.1016/j.proeng.2011.11.2496 – ident: ref3 doi: 10.1007/s11432-011-4323-7 – ident: ref16 doi: 10.1109/TSP.2004.833862 – year: 2007 ident: ref12 article-title: A Single Sensor Hand Biometric Multimodal System publication-title: IEEE – volume: 6 start-page: 20 issue: 2 year: 2017 ident: ref4 article-title: Negative Selection Algorithm: Recent Improvements and its Application in Intrusion Detection System publication-title: International Journal of Computing Academic Research – ident: ref2 doi: 10.48550/arXiv.1905.04717 – ident: ref1 doi: 10.1016/j.jksuci.2019.09.003 – volume-title: Proceedings of the World Congress on Engineering and Computer Science 2013 year: 2013 ident: ref5 article-title: Remote Access Control System Using Iris Signature – ident: ref13 doi: 10.1016/j.dsp.2004.05.001 – ident: ref9 doi: 10.1109/ICCICCT.2014.6993023 – ident: ref10 doi: 10.1007/s41870-020-00501-0
SSID	ssj0001255655
Score	2.2202408
Snippet	Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system...
SourceID	doaj crossref ceeol
SourceType	Open Website Index Database Publisher
StartPage	3
SubjectTerms	artificial immune system ICT Information and Communications Technologies negative selection algorithm nsa optimized negative selection algorithm recognition accuracy teaching-learning-based optimization algorithm
Title	Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System
URI	https://www.ceeol.com//search/article-detail?id=1211634 https://doaj.org/article/49bd981510444fb987412e47ae866fa9
Volume	12
WOSCitedRecordID	wos001106204000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1805-4951 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001255655 issn: 1805-4951 databaseCode: DOA dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1805-4951 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001255655 issn: 1805-4951 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELUQYmDhu6J8yRKsgSZ2YntsUSsYKEiAxERkOz4oapuqLQwM_HbOTorKxMISWXGUWO_s3J3le4-QM5PapGVtESVKphFnRkcKvUAEBljKjQCjZRCbEP2-fHpSd0tSX_5MWEUPXAF3wZUplES_5InNwGCKzOPEcaGdzDLQoXSvJdRSMlXtrqQYqfjzi7Fs4Rgwjqi5STGcFhdv53owwbYvoEYvUQ5_eaUl8v7gZXpbZKMOD2m7GtY2WXHjHbK5kF6g9UrcJc-3uNRHg09X0L57CeTd9D5I2iDOtD18KTHpfx1RDEnp9Qj_GTSoX_pzQcEUdDCmofZ2VBb4vY4vwvdc_bRiMN8jj73uw-VVVEslRJbFfB4lzPjQLQElXNGSVmjlmAbG0thYZ4zGHmkleiaBIYBLIAVjQGVWSG0L4KxBVsfl2O0TKiDWgnPDmANuIVYgJaQuYxBnQjDTJI0AWV5P9lnuWeIyxpvkdIFhPqmoMnKfYnjEMcdAxLGdNUnHw_vzhKe3DjfQ6Hlt9Pwvox_8x0sOybrXjq_2U47I6nz67o7Jmv2YD2bTkzCf8Hrz1f0GPX7QRg
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimized+Negative+Selection+Algorithm+for+Image+Classification+in+Multimodal+Biometric+System&rft.jtitle=Acta+Informatica+Pragensia&rft.au=Balogun%2C+Monsurat+Omolara&rft.au=Odeniyi%2C+Latifat+Adeola&rft.au=Omidiora%2C+Elijah+Olusola&rft.au=Olabiyisi%2C+Stephen+Olatunde&rft.date=2023-01-01&rft.issn=1805-4951&rft.eissn=1805-4951&rft.volume=12&rft.issue=1&rft.spage=3&rft.epage=18&rft_id=info:doi/10.18267%2Fj.aip.186&rft.externalDBID=n%2Fa&rft.externalDocID=10_18267_j_aip_186
thumbnail_s	http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.ceeol.com%2F%2Fapi%2Fimage%2Fgetissuecoverimage%3Fid%3Dpicture_2023_79934.png