Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications

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dc.contributor.authorDluhá, Jitkacs
dc.contributor.authorMalach, Tobiášcs
dc.coverage.issue15cs
dc.coverage.volume23cs
dc.date.issued2023-08-07cs
dc.description.abstractFace recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM’s application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4–10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.en
dc.formattextcs
dc.format.extent1-21cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2023, vol. 23, issue 15, p. 1-21.en
dc.identifier.doi10.3390/s23157012cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0002-8060-0086cs
dc.identifier.other184339cs
dc.identifier.researcheridF-9027-2019cs
dc.identifier.scopus23490150100cs
dc.identifier.urihttp://hdl.handle.net/11012/245019
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/23/15/7012cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjecttemplate creationen
dc.subjectsurveillance face recognitionen
dc.subjectclassifier learningen
dc.subjectparameter optimizationen
dc.subjectsecurity applicationen
dc.titleOptimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applicationsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-184339en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:41:50en
sync.item.modts2025.01.17 18:49:33en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav radioelektronikycs
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