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

Abraham, J; Champod, C; Lennard, C; Roux, C

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

Abraham, J Champod, C Lennard, C

Roux, C

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Publication Type:: Journal Article
Citation:: Forensic Science International, 2013, 230 (1-3), pp. 87 - 98
Issue Date:: 2013-07-01

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Field	Value	Language
dc.contributor.author	Abraham, J	en_US
dc.contributor.author	Champod, C	en_US
dc.contributor.author	Lennard, C https://orcid.org/0000-0002-9164-7103	en_US
dc.contributor.author	Roux, C https://orcid.org/0000-0003-3610-420X	en_US
dc.date.available	2012-10-18	en_US
dc.date.issued	2013-07-01	en_US
dc.identifier.citation	Forensic Science International, 2013, 230 (1-3), pp. 87 - 98	en_US
dc.identifier.issn	0379-0738	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26458
dc.description.abstract	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. © 2012 Elsevier Ireland Ltd.	en_US
dc.relation.ispartof	Forensic Science International	en_US
dc.relation.isbasedon	10.1016/j.forsciint.2012.10.034	en_US
dc.subject.classification	Legal & Forensic Medicine	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Dermatoglyphics	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Likelihood Functions	en_US
dc.subject.mesh	Support Vector Machines	en_US
dc.subject.mesh	Spatial Analysis	en_US
dc.subject.mesh	Support Vector Machine	en_US
dc.title	Spatial analysis of corresponding fingerprint features from match and close non-match populations	en_US
dc.type	Journal Article
utslib.citation.volume	1-3	en_US
utslib.citation.volume	230	en_US
utslib.for	0399 Other Chemical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	1-3	en_US
pubs.publication-status	Published	en_US
pubs.volume	230	en_US

Abstract:

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. © 2012 Elsevier Ireland Ltd.

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http://hdl.handle.net/10453/26458