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Statistical analysis of fingerprint first-level detail using Bayesian networks.

Keith B Morris1, Jamie S Spaulding2

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|November 10, 2025
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Fingerprint patterns show structured interdependence, not randomness. Bayesian networks reveal relationships between patterns, improving accuracy in biometric identification systems.

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ABISAFISBayesian networkfile penetration predictionfingerprintfirst‐level detailsearch depth

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Area of Science:

  • Forensic Science
  • Biometrics
  • Statistical Modeling

Background:

  • Fingerprint pattern distribution is traditionally considered random.
  • Limited research exists on the interdependence of fingerprint patterns within and across hands.
  • Sex-related differences in fingerprint pattern frequency require further investigation.

Purpose of the Study:

  • To statistically examine 168,974 tenprint records for pattern interdependence and sex-related differences.
  • To develop and validate Bayesian networks modeling relationships between fingerprint patterns.
  • To enhance automated biometric identification systems through statistical modeling.

Main Methods:

  • Large-scale statistical analysis of 168,974 tenprint records.
  • Empirical development and validation of two Bayesian networks.
  • Modeling of whorl occurrence and all major fingerprint patterns across fingers and hands.

Main Results:

  • Demonstrated significant inter- and intrahand relationships in fingerprint patterns.
  • Developed Bayesian networks that model probabilistic dependencies.
  • Validated expected relationships between pattern types, extending traditional classification.

Conclusions:

  • Fingerprint pattern distribution is not random but exhibits structured interdependence.
  • Bayesian networks can predict pattern occurrences, improving biometric search accuracy.
  • The study provides a novel approach to fingerprint analysis and biometric identification.