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Activity Number: 658 - Biometrics Data Mining
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #323487
Title: On the Forensic Analysis of Latent Fingerprint Evidence
Author(s): Alan Izenman*
Companies: Temple University
Keywords: ACE-V ; databases ; minimum spanning tree ; two-sample problem ; identification errors ; minutiae
Abstract:

Statistical thinking and practice can make a substantial contribution to the manner in which forensic science is handled in the laboratory and the courtroom. We first present some background on the history of fingerprint identification. Then, we describe the various types of impressions made by fingerprints and the automated techniques used to extract information. We set out the competing hypotheses used to compare a fingerprint found at a crime scene and a fingerprint from a potential suspect. The ACE-V system of latent fingerprint identification is described, and the errors in identification that have been made. We describe the fingerprint databases, such as AFIS, IAFIS, and NGI. We then propose a new method of matching minutiae of a pair of fingerprints by interpreting it as a two-sample problem in two dimensions. We adapt a graphical procedure that computes a nonparametric statistic R based upon a minimum spanning tree to the problem of matching a set of latent minutiae to a set of tenprint minutiae, and we apply the method to a set of fingerprint data. Suggestions are also made for estimating the standard error of R.


Authors who are presenting talks have a * after their name.

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