Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database to Enhance Lead Information from Automotive Paint Trace Evidence

Abstract

Multilayered automotive paint fragments, which are one of the most complex materials encountered in the forensic science laboratory, provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, forensic automotive paint examiners have turned to the paint data query (PDQ) database, which allows the forensic examiner to compare the layer sequence and color, texture, and composition of the sample to paint systems of the original equipment manufacturer (OEM). However, modern automotive paints have a thin color coat and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database in an effort to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach employing pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts.

Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint

“…The sym6 mother wavelet (Symlet wavelet family, sixth smallest filter size, eighth level of decomposition) was applied to each spectrum in the region 1641 cm À1 to 860 cm À1 as the region below 860 cm À1 in the image maps is noisy and the C=O stretch at 1730 cm À1 has been previously shown to be uninformative for discriminating vehicle manufacturer and assembly plant. 19,20 The wavelet coefficients (both the approximation and detailed coefficients) for the IR spectra of the clear coat, surfacer-primer, and e-coat layers were concatenated for each OEM paint in the in-house library to form the 1275 data vectors used to develop the search prefilters. Prior to pattern recognition analysis, the data vectors were autoscaled to ensure that each wavelet coefficient had a mean of zero and standard deviation of one.…”

“…Further details on the development and application of these search prefilters to OEM paints can be found elsewhere. 19,20,25 To demonstrate the performance of the assembly plant search prefilter, 25 a paint sample (UAZP00412-Dodge RAM) correctly identified as Chrysler by the manufacturer search prefilter system was assigned to one of two Chrysler assembly plant groups (Plant Group 11) and then to one of identified by the pattern recognition GA that is serving as the prefilter to identify the assembly plants comprising Plant Group 11 (Figure 8). For this pattern recognition problem, Saltillo and Toluca were combined into a single assembly plant because of the similarity of their IR spectra.…”

Infrared Handprint Image Restoration Algorithm Based on Apoptotic Mechanism

“…The work in [8] describes an innovative modeling method for discriminating the ATR FT-IR spectra of various body fluids, including peripheral blood, saliva, semen, urine and sweat, to meet the practical demands in criminal investigations. The work in [9] aimed at multilayered automotive paint fragments, which provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, its improved method permits intercomparison of OEM automotive paint layer systems using the IR spectra alone.…”

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

“…Previously published studies on the use of search prefilters for the PDQ database were largely restricted to the identification of the assembly plant of the vehicle. These studies typically involved only a single manufacturer and a single layer (clear coat layer) [9][10][11][12][13][14] or multiple layers (clear coat, surfacer-primer, and e-coat layer) [15][16][17][18] of automotive paint.…”

“…Previous studies of search prefilters applied to the PDQ database to identify the automotive manufacturer were limited to General Motors, Chrysler, and Ford. [16][17][18] The manufacturer search prefilter for PDQ described in these previous studies consisted of a single discriminant to solve a three-way classification problem where each class was a specific vehicle manufacturer spanning a limited production year range (2000)(2001)(2002)(2003)(2004)(2005)(2006). The significance of the current study in relation to the previously published studies on search prefilters for manufacturer arises from the complexity of the pattern recognition problem encountered when a larger number of automotive manufacturers are considered.…”