Accuracy and reliability of forensic latent fingerprint decisions
Open accessAbstract
The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. The National Research Council of the National Academies and the legal and forensic sciences communities have called for research to measure the accuracy and reliability of latent print examiners’ decisions, a challenging and complex problem in need of systematic analysis. Our research is focused on the development of empirical approaches to studying this problem. Here, we report on the first large-scale study of the accuracy and reliability of latent print examiners’ decisions, in which 169 latent print examiners each compared approximately 100 pairs of latent and exemplar fingerprints from a pool of 744 pairs. The fingerprints were selected to include a range of attributes and quality encountered in forensic casework, and to be comparable to searches of an automated fingerprint identification system containing more than 58 million subjects. This study evaluated examiners on key decision points in the fingerprint examination process; procedures used operationally include additional safeguards designed to minimize errors. Five examiners made false positive errors for an overall false positive rate of 0.1%. Eighty-five percent of examiners made at least one false negative error for an overall false negative rate of 7.5%. Independent examination of the same comparisons by different participants (analogous to blind verification) was found to detect all false positive errors and the majority of false negative errors in this study. Examiners frequently differed on whether fingerprints were suitable for reaching a conclusion.
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For the first time in some forensic disciplines, careful studies have attempted to estimate error rates.
“For the first time in some disciplines, careful studies have attempted to estimate error rates (Ulery et al. 2011).”
From Wrongful Convictions (2020) · cites this paper as Ulery et al. 2011
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A User Authentication Scheme Using Physiological and Behavioral Biometrics for Multitouch Devices
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Modelling the effects of crime type and evidence on judgments about guilt
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- Authors
- Ulery, B. T., Hicklin, R. A., Buscaglia, J., & Roberts, M. A.
- Affiliations
- Noblis (US)
- Federal Bureau of Investigation (US)
- Corresponding author
- Buscaglia, JoAnn
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-04-25
- Volume
- 108
- Issue
- 19
- Pages
- 7733-7738
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3093498
- OpenAlex
- W2009575609
- Cited by
- 350
- References
- 22
- Citations by year
- 2026: 24 · 2025: 17 · 2024: 29 · 2023: 23 · 2022: 35 · 2021: 31 · 2020: 40 · 2019: 21 · 2018: 16 · 2017: 20 · 2016: 17 · 2015: 23 · 2014: 21 · 2013: 20 · 2012: 10
- FWCI
- 18.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Social Sciences › Safety Research › Forensic Fingerprint Detection Methods
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- forensic evidence reliability, false positives, false negative errors, automated fingerprint identification systems, fingerprint comparison
- MeSH terms
- Dermatoglyphics, Expert Testimony, False Negative Reactions, False Positive Reactions, Humans, Professional Competence, Software, Reproducibility of Results, Observer Variation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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