Spatial analysis of corresponding fingerprint features from match and close non-match populations

Détails

ID Serval
serval:BIB_85AF746752DF
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Spatial analysis of corresponding fingerprint features from match and close non-match populations
Périodique
Forensic Science International
Auteur(s)
Abraham J., Champod C., Lennard C., Roux C.
ISSN
1872-6283
ISSN-L
0379-0738
Statut éditorial
Publié
Date de publication
07/2013
Peer-reviewed
Oui
Volume
230
Numéro
1-3
Pages
87-98
Langue
anglais
Résumé
The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework.
In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.
Mots-clé
Fingerprint identification, Likelihood ratio, Statistical models, Spatial analysis, Candidate lists
Création de la notice
01/07/2013 10:08
Dernière modification de la notice
20/08/2019 15:45
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