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Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

NCJ Number
253305
Date Published
2018
Length
13 pages
Author(s)
Barry K. Lavine; Collin G. White; Tao Ding
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2014-DN-BX-K087
Annotation
This study applied pattern-recognition techniques to the infrared (IR) spectral libraries of the Paint Data Query (PDQ) database to differentiate between nonidentical but similar IR spectra of automotive paints.
Abstract
To address the problem of library searching, search prefilters were developed to identify the vehicle make from IR spectra of the clear coat, surfacer-primer, and e-coat layers. To develop these search prefilters with the appropriate degree of accuracy, IR spectra from the PDQ database were preprocessed using the discrete wavelet transform to enhance subtle but significant features in the IR spectral data. Wavelet coefficients characteristic of vehicle make were identified using a genetic algorithm for pattern recognition and feature selection. Search prefilters to identify automotive manufacturer through IR spectra obtained from a paint chip recovered at a crime scene were developed using 1,596 original manufacturer's paint systems spanning six makes (General Motors, Chrysler, Ford, Honda, Nissan, and Toyota) within a limited production year range (2000-2006). Search prefilters for vehicle manufacturer that were developed as part of this study were successfully validated using IR spectra obtained directly from the PDQ database. Information obtained from these search prefilters can quantify the discrimination power of original automotive paint encountered in casework and further efforts to succinctly communicate trace evidential significance to the courts. (publisher abstract modified)
Date Created: July 20, 2021