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Measuring the Rarity of Fingerprint Patterns in the Dutch Population Using an Extended Classification Set
Author(s) -
de Jongh Arent,
Lubach Anko R.,
Lie Kwie Sheryl L.,
Alberink Ivo
Publication year - 2019
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13838
Subject(s) - fingerprint (computing) , matching (statistics) , identity (music) , computer science , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , minutiae , fingerprint recognition , population , data mining , information retrieval , statistics , mathematics , medicine , physics , environmental health , acoustics , programming language
Latent print examiners often use their experience and knowledge to reach a conclusion on the identity of the source. Their conclusion is primarily based on their personal opinion on the rarity of the matching fingerprint features. Fingerprint patterns, if present, can play a significant role in the final assessment of a match. The authors believe that statistical data on the rarity of fingerprint patterns strengthens the subjective evaluation of the corresponding information. In order to provide fingerprint examiners with additional numerical support, fingerprint patterns were manually classified in a set of 24,104 fingerprints. In this study the frequencies of occurrence of 35 different fingerprint patterns have been obtained. The frequency data presented in this study can be used in the ACE‐V process applied in forensic casework, allowing for the assessment of the evidential strength related to a specific fingerprint pattern type.