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

• Published in: Forensic science international Vol. 230; no. 1-3; pp. 87 - 98
• Main Authors: Abraham, Joshua, Champod, Christophe, Lennard, Chris, Roux, Claude
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 10.07.2013
• Subjects: Candidate lists; data collection; Dermatoglyphics; Fingerprint identification; Fingerprints; Forensic engineering; forensic sciences; Humans; Likelihood Functions; Likelihood ratio; Mathematical models; Models, Statistical; morphometry; Pathology; Populations; Spatial Analysis; Statistical analysis; Statistical models; Support Vector Machine; support vector machines; Likelihood ratio; Spatial analysis; Statistical models; Fingerprint identification; Candidate lists
• ISSN: 0379-0738; 1872-6283; 1872-6283
• DOI: 10.1016/j.forsciint.2012.10.034

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.