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Using Spatial, Temporal and Evidence‐status Data to Improve Ballistic Imaging Performance
Author(s) -
Yang Yan,
Koffman Avi,
Hocherman Gil,
Wein Lawrence M.
Publication year - 2014
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12269
Subject(s) - cartridge , matching (statistics) , constraint (computer aided design) , computer science , similarity (geometry) , identification (biology) , artificial intelligence , poison control , pattern recognition (psychology) , crime scene , data mining , computer vision , image (mathematics) , statistics , mathematics , engineering , geography , mechanical engineering , medicine , botany , geometry , environmental health , archaeology , biology
Firearms identification imaging systems help solve crimes by comparing newly acquired images of cartridge casings or bullets to a database of images obtained from past crime scenes. We formulate an optimization problem that bases its matching decisions not only on the similarity between pairs of images, but also on the time and spatial location of each new acquisition and each database entry. The objective is to maximize the detection probability subject to a constraint on the false positive rate. We use data on all cartridge casings matches detected in Israel during 2006–2008 to estimate most of the model parameters. We estimate matching accuracy from two different studies and predict that the optimal use of extraneous information would increase the detection probability from 0.931 to 0.987 and from 0.707 to 0.844, respectively. These improvements are achieved by favoring pairs of images that are closer together in space and time.