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A novel feature ranking algorithm for biometric recognition with PPG signals.

A Reşit Kavsaoğlu1, Kemal Polat2, M Recep Bozkurt1

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, M6 Building, 54187 Sakarya, Turkey.

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Summary

This study introduces a novel feature-ranking algorithm using Photoplethysmography (PPG) signals for accurate biometric identification. The method achieves high recognition rates, showing promise for contactless identity verification.

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BiometricsClassificationDerivativesFeature ExtractionIdentificationPhotoplethysmography (PPG)

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Area of Science:

  • Biometrics
  • Signal Processing
  • Machine Learning

Background:

  • Biometric identification systems are crucial for security.
  • Photoplethysmography (PPG) signals offer a non-invasive method for physiological monitoring.
  • Developing robust and accurate biometric identification methods is an ongoing challenge.

Purpose of the Study:

  • To apply Photoplethysmography (PPG) signals and their time-domain features for biometric identification.
  • To propose a novel feature-ranking algorithm to enhance recognition accuracy.
  • To evaluate the effectiveness of the proposed method using a k-nearest neighbor (k-NN) classifier.

Main Methods:

  • Extraction of 40 time-domain features from PPG signal derivatives.
  • Development of a feature-ranking algorithm using Euclidean and absolute distances.
  • Utilizing PPG signals from 30 healthy subjects across three configurations for k-NN classification.

Main Results:

  • The proposed algorithm achieved high identification accuracy: 90.44% (1st config), 94.44% (2nd config), and 87.22% (3rd config).
  • The feature-ranking algorithm effectively identified and prioritized significant features for biometric recognition.
  • The k-NN classifier demonstrated strong performance with the selected PPG features.

Conclusions:

  • The proposed algorithm and PPG-based biometric model are highly promising for contactless human recognition.
  • PPG signal analysis offers a viable and accurate approach for developing advanced biometric systems.
  • The developed method provides a foundation for future research in non-invasive biometric identification.