Wavelets and genetic algorithms applied to search prefilters for spectral library matching in forensics

摘要

Currently, the identification of the make, model and year of a motor vehicle involved in a hit and run collision from only a clear coat paint smear left at a crime scene is not possible. Search prefilters for searching infrared (IR) spectral libraries of the paint data query (PDQ) automotive database to differentiate between similar but nonidentical Fourier transform infrared (FTIR) paint spectra are proposed. Applying wavelets, FTIR spectra of clear coat paint smears can be denoised and deconvolved by decomposing each spectrum into wavelet coefficients which represent the sample's constituent frequencies. A genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients for underdetermined data that are characteristic of the model and manufacturer of the automobile from which the spectra of the clear coats were obtained. Even in challenging trials where the samples evaluated were all the same manufacturer (Chrysler) with a limited production year range, the respective models and manufacturing plants were correctly identified. Search prefilters for spectral library matching are necessary to extract investigative lead information from a clear coat paint smear; unlike the undercoat and color coat paint layers, which can be identified using the text based portion of the PDQ database.