Academic Journal
View in EDS

QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems.

Bibliographic Details
Title: QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems.
Authors: Nagaraja, Prajwalasimha Sindugatta, Kulkarni, Naveen, Ichangi, Raghavendra M., Varanamkudath, Vinitha, Tadkal, Sharanabasappa, Parakkal, Ranjima, Karuppusamy, Deepthika
Source: Bulletin of Electrical Engineering & Informatics; Jun2025, Vol. 14 Issue 3, p1959-1968, 10p
Subject Terms: Iris recognition, Image denoising, Burst noise, Image recognition (Computer vision), Spatial filters
Abstract: This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems. [ABSTRACT FROM AUTHOR]
Copyright of Bulletin of Electrical Engineering & Informatics is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
FullText Text:
  Availability: 0
Header DbId: edb
DbLabel: Complementary Index
An: 185272553
RelevancyScore: 1007
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1007.33093261719
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Nagaraja%2C+Prajwalasimha+Sindugatta%22">Nagaraja, Prajwalasimha Sindugatta</searchLink><br /><searchLink fieldCode="AR" term="%22Kulkarni%2C+Naveen%22">Kulkarni, Naveen</searchLink><br /><searchLink fieldCode="AR" term="%22Ichangi%2C+Raghavendra+M%2E%22">Ichangi, Raghavendra M.</searchLink><br /><searchLink fieldCode="AR" term="%22Varanamkudath%2C+Vinitha%22">Varanamkudath, Vinitha</searchLink><br /><searchLink fieldCode="AR" term="%22Tadkal%2C+Sharanabasappa%22">Tadkal, Sharanabasappa</searchLink><br /><searchLink fieldCode="AR" term="%22Parakkal%2C+Ranjima%22">Parakkal, Ranjima</searchLink><br /><searchLink fieldCode="AR" term="%22Karuppusamy%2C+Deepthika%22">Karuppusamy, Deepthika</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Bulletin of Electrical Engineering & Informatics; Jun2025, Vol. 14 Issue 3, p1959-1968, 10p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Iris+recognition%22">Iris recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Burst+noise%22">Burst noise</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+filters%22">Spatial filters</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Bulletin of Electrical Engineering & Informatics is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=185272553
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.11591/eei.v14i3.9036
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1959
    Subjects:
      – SubjectFull: Iris recognition
        Type: general
      – SubjectFull: Image denoising
        Type: general
      – SubjectFull: Burst noise
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
      – SubjectFull: Spatial filters
        Type: general
    Titles:
      – TitleFull: QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Nagaraja, Prajwalasimha Sindugatta
      – PersonEntity:
          Name:
            NameFull: Kulkarni, Naveen
      – PersonEntity:
          Name:
            NameFull: Ichangi, Raghavendra M.
      – PersonEntity:
          Name:
            NameFull: Varanamkudath, Vinitha
      – PersonEntity:
          Name:
            NameFull: Tadkal, Sharanabasappa
      – PersonEntity:
          Name:
            NameFull: Parakkal, Ranjima
      – PersonEntity:
          Name:
            NameFull: Karuppusamy, Deepthika
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 20893191
          Numbering:
            – Type: volume
              Value: 14
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Bulletin of Electrical Engineering & Informatics
              Type: main
ResultId 1