Automatic forensic analysis of automotive paints using optical microscopy. (February 2016) - Thoonen, Guy; Nys, Bart; Vander Haeghen, Yves; De Roy, Gilbert; Scheunders, Paul
Abstract : Highlights: A method for automating visual analysis of automotive paint samples is proposed. The technique combines color calibration and extraction of color/texture features. The features are used to query a database of paints for the best possible match. The outcome is a shortlist of matches suggested for further investigation. Abstract: The timely identification of vehicles involved in an accident, such as a hit-and-run situation, bears great importance in forensics. To this end, procedures have been defined for analyzing car paint samples that combine techniques such as visual analysis and Fourier transform infrared spectroscopy. This work proposes a new methodology in order to automate the visual analysis using image retrieval. Specifically, color and texture information is extracted from a microscopic image of a recovered paint sample, and this information is then compared with the same features for a database of paint types, resulting in a shortlist of candidate paints. In order to demonstrate the operation of the methodology, a test database has been set up and two retrieval experiments have been performed. The first experiment quantifies the performance of the procedure for retrieving exact matches, while the second experiment emulates the real-life situation of paint samples that experience changes in color and texture over time.