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IR Frequency Region: Fingerprint Region01:03

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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...
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Supporting fingerprint identification assessments using a skin stretch model - A preliminary study.

Rebecca Lee1, Bruce Comber2, Joshua Abraham1

  • 1Centre for Forensic Science, University of Technology Sydney, Sydney, NSW, Australia.

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|January 23, 2017
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Summary

This study introduces a statistical method to enhance fingerprint analysis, combining human expertise with probability models to objectively assess distortion. The new metric reliably distinguishes between genuine and fake fingerprints, supporting expert opinions in casework.

Keywords:
DistortionFingermarkMathematical modelsMinutiaeRidge characteristicsStatistics

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Area of Science:

  • Forensic Science
  • Biometrics
  • Statistical Modeling

Background:

  • Fingerprint analysis relies on subjective expert opinion.
  • Quantifying distortion in fingerprints is challenging.
  • Objective methods are needed to support fingerprint examination.

Purpose of the Study:

  • To develop a statistical approach for fingerprint analysis.
  • To objectively assess fingerprint distortion caused by skin stretch.
  • To support and validate fingerprint expert opinions.

Main Methods:

  • A statistical model using multivariate normal probability density function was applied.
  • The model analyzed distances and angles between ridge characteristics and neighboring minutiae.
  • The approach was tested on data from 5 donors, comparing within-source and between-source comparisons.

Main Results:

  • An "expected range" for distortion in within-source comparisons (10 minutiae) was determined (-33.4 to -60.0 log probability densities).
  • Between-source comparisons consistently fell outside this range (-83 to -305 log probability densities).
  • The proposed metric demonstrated a clear distinction between genuine and non-genuine comparisons.

Conclusions:

  • The developed statistical metric can effectively assess fingerprint distortion.
  • This approach provides an objective tool to support fingerprint expert opinion in casework.
  • The findings suggest enhanced reliability and objectivity in fingerprint identification.