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Research on likelihood ratio evaluation method of fingerprint evidence based on parameter estimation method.

Kang Li1,2, Yishi Han2, Yaping Luo1

  • 1School of Investigation, People's Public Security University of China, Beijing, China.

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|March 28, 2024
PubMed
Summary

The likelihood ratio (LR) model enhances fingerprint identification accuracy by using statistical methods. Increasing minutiae number improves accuracy, making fingerprint analysis more scientific and reducing misidentification risks.

Keywords:
evidence evaluationfingerprintshypothesis testinglikelihood ratioparameter estimationquantitative method

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Area of Science:

  • Forensic Science
  • Biometrics
  • Statistical Modeling

Background:

  • Individual identification from fingerprints is prone to errors, particularly with large databases.
  • Similar morphological characteristics in fingerprints from different individuals pose identification challenges.
  • The likelihood ratio (LR) model offers a quantitative approach to evaluate fingerprint evidence.

Purpose of the Study:

  • To establish and evaluate a likelihood ratio (LR) fingerprint evidence evaluation model.
  • To enhance the accuracy of identifying similar fingerprints from large databases.
  • To transition fingerprint identification from an experience-based practice to a scientific one.

Main Methods:

  • Mathematical statistical methods including parameter estimation and hypothesis testing were employed.
  • Database construction, scoring, fitting, calculation, and visual evaluation were performed.
  • Optimal parameter methods (gamma, Weibull, normal, lognormal distributions) were selected based on minutiae number and configuration under same-source and different-source conditions.

Main Results:

  • The LR model demonstrated increased accuracy with a higher number of minutiae, showing strong discriminative and corrective power.
  • LR evaluation accuracy based on different minutiae configurations was comparatively lower.
  • LR models utilizing the number of minutiae outperformed those based on minutiae configurations.

Conclusions:

  • The use of parametric LR models is favored for reducing fingerprint misidentification.
  • The study improves quantitative assessment methods for fingerprint evidence.
  • The LR model promotes a more scientific approach to fingerprint identification.