Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

Highlights • This paper proposes a feature-based fingerprint statistical model. • The model has been using a very large reference dataset of finger impressions. • The dataset contained all finger numbers and all friction ridge patterns. • The results show that the model has low rates of misleading e...

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Bibliographic Details
Published in:	Forensic science international Vol. 248; pp. 154 - 171
Main Authors:	Neumann, Cedric, Champod, Christophe, Yoo, Mina, Genessay, Thibault, Langenburg, Glenn
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier Ireland Ltd 01-03-2015 Elsevier Limited
Subjects:	Algorithms Deoxyribonucleic acid Dermatoglyphics DNA Estimates Fingerprint evidence Fingerprinting Fingerprints Fingers Fingers & toes Forensic science Forensic sciences Humans Likelihood Functions Models, Statistical Pathology Random variables Samples Spatial Analysis Spatial relationship Statistical analysis Statistical model Strength of evidence Studies Sub-population effect Strength of evidence Fingerprint evidence Spatial relationship Sub-population effect Statistical model
Online Access:	Get full text
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Summary:	Highlights • This paper proposes a feature-based fingerprint statistical model. • The model has been using a very large reference dataset of finger impressions. • The dataset contained all finger numbers and all friction ridge patterns. • The results show that the model has low rates of misleading evidence. • The overall results show that fingerprint evidence is extremely valuable.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0379-0738 1872-6283
DOI:	10.1016/j.forsciint.2015.01.007