Literature DB >> 32512413

Assessing the frequency of general fingerprint patterns by fingerprint examiners and novices.

Erwin J A T Mattijssen1, Cilia L M Witteman2, Charles E H Berger3, Reinoud D Stoel4.   

Abstract

The rarity of general fingerprint patterns should be taken into account in the assessment of fingerprint evidence to provide a more complete assessment of fingerprint evidence than when only considering the minutiae. This should be done because, the rarer the corresponding pattern, the stronger the support for the hypothesis that the fingermark stems from the same source as the reference fingerprint. Fingerprint examiners' experience should enable them to provide meaningful assessments of the frequencies of these general patterns according to the theories of perceptual learning, exemplar theory of categorization and visual statistical learning. In this study we examined the accuracy of fingerprint examiners' and novices' judgments on the rarity of general fingerprint patterns. We found that fingerprint examiners seem to have acquired some knowledge about the rarity of general patterns, but had difficulty expressing this knowledge quantitatively using a novel sub-classification of general patterns. As a consequence, their judgments were not accurate and they did not perform better on this task than novices. For both participant groups judgments of more common patterns were more accurate. However, examiners did outperform novices in rank ordering general patterns from common to rare. We conclude that our study does not show that fingerprint examiners have expertise in explicitly judging frequencies of novel sub-classifications of general fingerprint patterns, but our results do indicate that the examiners have acquired knowledge about the rarity of patterns that novices do not possess.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Categorization; Fingerprint expertise; General fingerprint pattern; Judgment; Perceptual learning; Visual statistical learning

Mesh:

Year:  2020        PMID: 32512413     DOI: 10.1016/j.forsciint.2020.110347

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  3 in total

1.  Statistical feature training improves fingerprint-matching accuracy in novices and professional fingerprint examiners.

Authors:  Bethany Growns; Alice Towler; James D Dunn; Jessica M Salerno; N J Schweitzer; Itiel E Dror
Journal:  Cogn Res Princ Implic       Date:  2022-07-16

Review 2.  Human factors in forensic science: The cognitive mechanisms that underlie forensic feature-comparison expertise.

Authors:  Bethany Growns; Kristy A Martire
Journal:  Forensic Sci Int Synerg       Date:  2020-05-21

3.  Match me if you can: Evidence for a domain-general visual comparison ability.

Authors:  Bethany Growns; James D Dunn; Erwin J A T Mattijssen; Adele Quigley-McBride; Alice Towler
Journal:  Psychon Bull Rev       Date:  2022-01-07
  3 in total

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