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Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system

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dc.citedby2
dc.contributor.authorAbdulhasan R.A.en_US
dc.contributor.authorAbd Al-latief S.T.en_US
dc.contributor.authorKadhim S.M.en_US
dc.contributor.authorid56905439800en_US
dc.contributor.authorid58590896700en_US
dc.contributor.authorid58590009300en_US
dc.date.accessioned2025-03-03T07:44:19Z
dc.date.available2025-03-03T07:44:19Z
dc.date.issued2024
dc.description.abstractBiometric-based identity verification systems have gained substantial attention due to their ability to provide high-level security. Among these systems, iris recognition systems have emerged as one of the most accurate and complex verification approaches. However, an ideal recognition system with a short processing time has not yet been reported in the literature because of the trade-offs involved. In this article, a novel framework for an iris recognition system is proposed based on hybrid deep neural network (DNN) classification-based Linear Discriminant Analysis (LDA) for feature extraction. The developed system includes unique pre-processing steps for both training and testing datasets, which are modulated by greyscale conversation, Gaussian blurring, binary imaging, contour segmentation and resizing. The proposed LDA-DNN provides high accuracy and stability for human identity verification with a short processing time. The proposed model accomplishes this task perfectly without any loss, which is unique among this type of approach. The results are validated via five computed measurement parameters. Experimental results are obtained by applying the model to four typical existing databases for powerful validation. Moreover, the proposed LDA-DNN framework results are compared with outcome measures obtained for state-of-the-art iris recognition approaches. The experimental results illustrate the success and power of the proposed LDA-DNN model, which attains an accuracy of 100% within a time of 70 ms, corresponding to an ideal recognition result that validated using several databases. Furthermore, this work provides a model within a unique property, in which it does not require a specific database or measurement parameters for evaluation. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11042-023-16751-6
dc.identifier.epage32122
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85171424677
dc.identifier.spage32099
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171424677&doi=10.1007%2fs11042-023-16751-6&partnerID=40&md5=1b9a48a4de7c702a768852ced592ace3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36740
dc.identifier.volume83
dc.pagecount23
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleMultimedia Tools and Applications
dc.subjectBinary images
dc.subjectBiometrics
dc.subjectDatabase systems
dc.subjectDiscriminant analysis
dc.subjectEconomic and social effects
dc.subjectExtraction
dc.subjectParameter estimation
dc.subjectDeep neural network deep neural network
dc.subjectGaussian blur
dc.subjectIdentity verification
dc.subjectImage contour
dc.subjectIris recognition
dc.subjectIris recognition systems
dc.subjectLinear discriminant analyze
dc.subjectLinear discriminate analyse linear discriminant analyse
dc.subjectLinear discriminate analysis
dc.subjectShortest Processing Time
dc.subjectDeep neural networks
dc.titleInstant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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