Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 71,
  • Issue 3,
  • pp. 480-495
  • (2017)

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

Not Accessible

Your library or personal account may give you access

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.

© 2016 The Author(s)

PDF Article
More Like This
Nondestructive analysis of automotive paints with spectral domain optical coherence tomography

Yue Dong, Samuel Lawman, Yalin Zheng, Dominic Williams, Jinke Zhang, and Yao-Chun Shen
Appl. Opt. 55(13) 3695-3700 (2016)

Improved measurement accuracy in optical scatterometry using correction-based library search

Xiuguo Chen, Shiyuan Liu, Chuanwei Zhang, and Hao Jiang
Appl. Opt. 52(27) 6726-6734 (2013)

Non-destructive analysis of flake properties in automotive paints with full-field optical coherence tomography and 3D segmentation

Jinke Zhang, Bryan M. Williams, Samuel Lawman, David Atkinson, Zijian Zhang, Yaochun Shen, and Yalin Zheng
Opt. Express 25(16) 18614-18628 (2017)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved