Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

• NCJ Number: 249282
• Journal: Applied Spectroscopy Volume: 69 Issue: 1 Dated: January 2015 Pages: 84-94
• Author(s): Ayuba Fasasi; Nikhil Mirjankar; Razvan-lonut Stoian; Collin White; Matthew Allen; Mark P. Sandercock; Barry K. Lavine
• Date Published: January 2015
• Length: 11 pages
• Grant Number(s): 2010-DN-BX-K217
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Evaluation
• Award: Improving Investigative Lead Information and Evidential Significance Assessment for Automotive Paint and the PDQ Database

Annotation

Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, the current study preprocessed spectra from the PDQ database using wavelets to enhance subtle but significant features in the data.

Abstract

Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window were compiled, and a histogram was computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence were selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum. (Publisher abstract modified)