title: Spatial analysis of corresponding fingerprint features from match and close non-match populations.
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.
authors.affiliation: Centre for Forensic Science, University of Technology Sydney, PO Box 123, Broadway NSW 2007, Australia. joshua.abraham@uts.edu.au
authors.forename: Joshua
authors.initials: J
authors.lastname: Abraham
authors.forename: Christophe
authors.initials: C
authors.lastname: Champod
authors.forename: Chris
authors.initials: C
authors.lastname: Lennard
authors.forename: Claude
authors.initials: C
authors.lastname: Roux
mesh_terms: D003878:Dermatoglyphics; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D062206:Spatial Analysis; D060388:Support Vector Machine