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A Machine Learning Approach Facilitating Contactless and Contact-Based Fingerprint Recognition through Magnitude Spectrum.

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Title: A Machine Learning Approach Facilitating Contactless and Contact-Based Fingerprint Recognition through Magnitude Spectrum.
Authors: Singh, Payal1, Diwakar Agarwal1, Ajitesh Kumar2 ajitesh.kumar@gla.ac.in
Source: International Journal of Performability Engineering. Jun2026, Vol. 22 Issue 6, p352-361. 10p.
Subjects: Support vector machines, Discrete Fourier transforms, Human fingerprints, Image processing, COVID-19, Biometric identification, Machine learning
Abstract: Matching contactless fingerprint images with traditional contact-based impressions has become increasingly important, especially due to the COVID-19 pandemic. Contactless methods provide better hygiene and benefit from the availability of affordable mobile phones capable of capturing high-resolution fingerprints. Traditional minutiae-based matching techniques are susceptible to errors caused by false or missing minutiae points in low-quality images, emphasizing the need for alternative features. This study explores the magnitude spectrum, a feature derived from the Discrete Fourier Transform (DFT) for matching contactless and contact-based fingerprints. A 256-bin histogram of the magnitude spectrum is generated to estimate the correlation distance to distinguish genuine from imposter attempts. Using this dataset, a Support Vector Machine (SVM) model is carefully trained and tested through 10-fold cross-validation. The results demonstrate a high matching accuracy of 97.97%, with an Equal Error Rate (EER) of 2.02% and a Rank 1 accuracy of 93.38%. The SVM classifier also achieves 96.96% accuracy in differentiating between 'genuine' and 'imposter' classes. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: Engineering Source
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  Data: A Machine Learning Approach Facilitating Contactless and Contact-Based Fingerprint Recognition through Magnitude Spectrum.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Jun2026, Vol. 22 Issue 6, p352-361. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+Fourier+transforms%22">Discrete Fourier transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Human+fingerprints%22">Human fingerprints</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Matching contactless fingerprint images with traditional contact-based impressions has become increasingly important, especially due to the COVID-19 pandemic. Contactless methods provide better hygiene and benefit from the availability of affordable mobile phones capable of capturing high-resolution fingerprints. Traditional minutiae-based matching techniques are susceptible to errors caused by false or missing minutiae points in low-quality images, emphasizing the need for alternative features. This study explores the magnitude spectrum, a feature derived from the Discrete Fourier Transform (DFT) for matching contactless and contact-based fingerprints. A 256-bin histogram of the magnitude spectrum is generated to estimate the correlation distance to distinguish genuine from imposter attempts. Using this dataset, a Support Vector Machine (SVM) model is carefully trained and tested through 10-fold cross-validation. The results demonstrate a high matching accuracy of 97.97%, with an Equal Error Rate (EER) of 2.02% and a Rank 1 accuracy of 93.38%. The SVM classifier also achieves 96.96% accuracy in differentiating between 'genuine' and 'imposter' classes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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.)
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      – Type: doi
        Value: 10.23940/ijpe.26.06.p6.352361
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 352
    Subjects:
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Discrete Fourier transforms
        Type: general
      – SubjectFull: Human fingerprints
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Biometric identification
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: A Machine Learning Approach Facilitating Contactless and Contact-Based Fingerprint Recognition through Magnitude Spectrum.
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            NameFull: Singh, Payal
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            NameFull: Diwakar Agarwal
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            NameFull: Ajitesh Kumar
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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            – TitleFull: International Journal of Performability Engineering
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