Skip to main content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Statistical Friction Ridge Analysis (SFRA)

Summary

This project aims to develop a statistical measure of the uncertainty of the decisions made on the friction ridge evidence (i.e., evidential value of fingerprint comparison), which ultimately can be referred to as a scientific basis of the identification decisions made in friction ridge analysis.

Description

Introduction

Friction ridge analysis in crime scene investigation is one of the crucial forensic methods to find the suspects and victims of crime and solve cases. Latent fingerprints—the friction ridge patterns from fingertips left at crime scenes and photographed or lifted from the surfaces—are commonly examined by following a methodology called Analysis, Comparison, Evaluation, and Verification (ACE-V). The ACE-V methodology involves latent examiners to (i) assess the value of latent fingerprints, (ii) mark features in the latent images, and (iii) make identification decisions on the pairs of latent and its potential mates retrieved from reference databases. The critical decisions made by the latent examiners include:

  • Latent value determination in the analysis phase: A latent examiner evaluates the suitability or sufficiency of a given latent fingerprint as forensic evidence, and makes one of the following decisions on the latent fingerprint: value for individualization (VID), value for exclusion only (VEO), or no value (NV). Only if the latent is determined as either VID or VEO, it is further processed and goes through comparisons with possible mates in the reference database.
  • Identification decision in the evaluation phase: A latent examiner makes one of the following decisions on each pair of latent and candidate mates: individualization (the latent and the candidate mate are deemed to come from the same source), exclusion (the pair is deemed to be from two distinct fingers), or inconclusive (neither individualization nor exclusion decision can be made).

Although friction ridge patterns as forensic evidence are highly admissible in courts of law, the lack of scientific basis of the ACE-V procedure has brought concerns due to subjectivity in latent examiner's decisions (an examiner's decision can be affected by the level of expertise, bias after exposure to additional information of the case, workload that hampers his focus and attention to detail, etc.) [2] and inconsistency (an examiner's decisions made on the same pair of fingerprints at different time points may not be identical; or multiple examiners' decisions on a single pair of prints may not be unanimous) [3]. The 2009 National Academy of Science report [4]pointed out the increased need for scientific research in the evaluation of methods used in forensic science, such as bias quantification, validation, and estimates of accuracy and precision in different contexts. In view of the Daubert ruling [5], forensic evidence is being subjected to rigorous standards, including empirical testing, known or potential error rate, and standards controlling the analysis procedure.

Purpose and Scope

A need for the scientific underpinning of friction ridge analysis in forensics has been greatly emphasized in order to achieve the robustness and transparency in procedure and decision making in latent fingerprint examination. Towards this goal, this project aims to develop a statistical measure of the uncertainty of the decisions made on the friction ridge evidence (i.e., evidential value of fingerprint comparison), which ultimately can be referred to as a scientific basis of the identification decisions made in friction ridge analysis.

For a pair of latent and fingerprint from suspect, the evidential value of fingerprint comparison is comprised of three components:

  1. a probability of observing the given evidence under the hypothesis that the latent print and the potential mate came from the same finger,
  2. a probability of observing the evidence under the hypothesis that the two prints came from two distinct fingers, and
  3. the strength or reliability of the observed evidence.

In other words, we would like to answer the following question: Given a pair of latent print and its potential mate from the suspect, what is the probability that (i) we observe this evidence in favor of individualization decision, (ii) we observe this evidence in favor of exclusion decision, and (iii) the evidence does not lead to either of individualization or exclusion decisions, but to inconclusive decision.

The ratio of 1) to 2) is known as likelihood ratio which is widely used in DNA analysis and has been recently introduced to friction ridge analysis by Champod and Neumann [6, 7].

Research Plan

In order to establish the three components of the evidential value of fingerprint comparisons, we will conduct the research in two phases, namely population study and evidential value assessment of latent comparisons.

Phase I: Population Study

  • The variability existing in fingerprints in nature will be studied. More specifically, we will conduct a population study to understand:
  1. Intra-class variability due to skin distortion
  2. Inter-class variability, in particular, close non-mated fingerprints that two impostor fingers share partial ridge structure.
  • Phase II: Evidential value assessment of latent comparisons
    We will introduce the quality of latent print to gauge the reliability of the observation and develop objective criteria to make a decision on a latent fingerprint pair.

Expected Impacts

  • Standards in friction ridge analysis and universal vocabulary for explaining the uncertainty in the court
  • A forum for exchange of information with other USG experts/projects on friction ridge to collaborate and complement work and avoid duplication,
  • Sharing resources, particularly data, to the extent possible.

Databases

We will update our results on the following databases.

Activities/News

  • Seminars
    • Prof. Christophe Champod, University of Lausanne, Switzerland "The Future of Delivering Fingerprint Evidence: A Roadmap Towards Twenty20" July 25, 2014

References and Related Work

  1. Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), "Standards for Examining Friction Ridge Impressions and Resulting Conclusions (Latent/Tenprint)", Version 2.0,March 2013.
  2. NIST and NIJ, "Latent Print Examination and Human Factors: Improving the Practice through a Systems Approach", Feb. 2012.
  3. B. T. Ulery, R. A. Hicklin, J. Buscaglia, and M.A. Roberts, "Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners", PLoS ONE, 7(3), 2012.
  4. National Research Council, "Strengthening Forensic Science in the United States: A Path Forward", 2009.
  5. Daubert v. Merrell Dow Pharmaceuticals Inc. 509U.S. 579, 1993. [Online: http://www.law.cornell.edu/supct/html/92-102.ZO.html]
  6. C. Neumann, C. Champod, R. Puch-Solis, N. Egli, A. Anthonioz, and A. Bromage-Griffiths, "Computation of Likelihood Ratios in Fingerprint Identification for Configurations of Any Number of Minutiae" , Journal of Forensic Science, 52(1):54–64, 2007.
  7. C. Neumann, I. W. Evett, J. Skerrett,"Quantifying the weight of evidence from a forensic fingerprint comparison: anew paradigm", Journal of Royal Statistical Society, 175(2): 371–415, 2012.
  8. C. Neumann, C. Champod, M. Yoo, T. Genessay, G. Langenburg, "Improving the Understanding and the Reliability of the Concept of 'Sufficiency' in Friction Ridge Examination", Final Report, NIJ Award Number2010-DN-BX-K267, 2013.
  9. D. V. Lindley, "A problem in Forensic Science", Biometrika, 64(2): 207–213, 1977.
  10. S. Yoon, K. Cao, E. Liu ,and A. K. Jain, "LFIQ: Latent Fingerprint Image Quality", IEEE International Conference on Biometrics: Theory, Applications and Systems, 2013.

[1] SWGFAST defines suitability or sufficiency as the determination that there is adequate quality and quantity of detail in an impression for further analysis, comparison or to reach a conclusion [1].

Created October 2, 2014, Updated November 7, 2019