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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2024 - 10
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Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features

KADHIM, O. N. See more information about KADHIM, O. N. on SCOPUS See more information about KADHIM, O. N. on IEEExplore See more information about KADHIM, O. N. on Web of Science, ABDULAMEER, M. H. See more information about ABDULAMEER, M. H. on SCOPUS See more information about ABDULAMEER, M. H. on SCOPUS See more information about ABDULAMEER, M. H. on Web of Science
 
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Download PDF pdficon (1,160 KB) | Citation | Downloads: 440 | Views: 308

Author keywords
deep learning, feature extraction, feature level fusion, multimodal biometrics identification, machine learning

References keywords
biometric(14), recognition(13), face(12), multimodal(10), fusion(10), system(7), learning(7), deep(7), vein(5), palmprint(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 91 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01010
SCOPUS ID: 85189474643

Abstract
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Due to increased information security concerns, biometric recognition technology has become more important. Unimodal biometrics still work effectively, but they struggle with noise sensitivity and spoof attack susceptibility since they rely on a single data source. This paper uses advances in deep learning and machine learning to propose new unimodal systems for the palm, face, and iris. These models use deep wavelet transform networks (WTN) for face and iris identification and deep convolutional neural networks (CNNs) for palmprint identification. In addition, we introduce a novel multimodal biometric system based on unimodal systems. We get 98.29% for face, 98.86% for palmprint, and 95.59% for iris in individual unimodal systems with Support Vector Machines (SVM). This is done by using the new property MULB dataset, which has many biometric features. The multimodal system achieves 99.88% accuracy and a 0.0186 equal error rate, underscoring the relevance of several biometric features and the superior performance of the identification system.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 26]


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[CrossRef] [Full Text] [SCOPUS Times Cited 1]


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[CrossRef]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 49]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]


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[CrossRef] [SCOPUS Times Cited 1]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8]






References Weight

Web of Science® Citations for all references: 2,191 TCR
SCOPUS® Citations for all references: 5,524 TCR

Web of Science® Average Citations per reference: 76 ACR
SCOPUS® Average Citations per reference: 190 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-26 19:25 in 175 seconds.




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