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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

697
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
697

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Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face

Sonal1, Ajit Singh2, Chander Kant3

  • 1Department of Computer Science & Engineering and Information Technology, Uttarakhand Technical University, Dehradun, Uttarakhand, India.

Peerj. Computer Science
|March 10, 2025
PubMed
Summary

This study presents a hybrid multi-biometric system using fingerprint, face, and iris recognition for enhanced security. The combined approach offers superior accuracy and reliability compared to single methods.

Keywords:
Face authenticationFingerprint authenticationGabor filtersIris recognitionMachine learningMulti-biometricRandom forestSupport vector machine

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Area of Science:

  • Computer Science
  • Biometrics
  • Security Engineering

Background:

  • Uni-modal biometric systems face limitations in accuracy and security.
  • Multi-biometric systems offer enhanced performance by combining multiple modalities.
  • Robust individual authentication is critical for secure real-world applications.

Purpose of the Study:

  • To introduce a hybrid multi-biometric system integrating fingerprint, face, and iris recognition.
  • To improve individual authentication accuracy, security, and reliability.
  • To address the limitations of uni-modal biometric approaches.

Main Methods:

  • Feature-level fusion of fingerprint, face, and iris data.
  • Utilizing Gabor filters for effective feature extraction.
  • Employing Support Vector Machine (SVM) and Random Forest (RF) classifiers.
  • Optimizing classifier performance with Bacterial Foraging Optimization (BFO) and Genetic Algorithms (GA).

Main Results:

  • The hybrid system demonstrates superior performance and heightened security.
  • Enhanced accuracy and reliability in individual authentication.
  • Improved robustness and efficiency through classifier integration and optimization techniques.

Conclusions:

  • The proposed hybrid multi-biometric system provides a dependable and resilient solution for secure identification.
  • Combining multiple biometric modalities significantly outperforms uni-modal systems.
  • The system is well-suited for real-world applications demanding high levels of security and accuracy.