Automatic forensic analysis of automotive paints using optical microscopy

Area of Science:

• Forensic Science
• Image Analysis
• Materials Science

Background:

• Accurate vehicle identification is crucial in forensic investigations, particularly for hit-and-run incidents.
• Current methods for analyzing car paint samples involve visual analysis and Fourier transform infrared spectroscopy.
• Automating visual analysis can enhance the efficiency and accuracy of forensic casework.

Purpose of the Study:

• To develop and demonstrate an automated methodology for visual analysis of car paint samples using image retrieval.
• To extract and compare color and texture features from microscopic paint images against a database.
• To create a shortlist of candidate paints for forensic identification.

Main Methods:

• Image retrieval techniques were employed to automate the visual analysis of car paint samples.
• Color and texture information was extracted from microscopic images of paint samples.
• A test database of paint types was established for comparison and retrieval experiments.

Main Results:

• The methodology successfully retrieved exact matches for paint samples in initial experiments.
• A second experiment simulated real-world conditions, evaluating performance with aged paint samples exhibiting altered color and texture.
• The system demonstrated potential in identifying candidate paints even with variations over time.

Conclusions:

• Automated visual analysis using image retrieval offers a promising approach for forensic car paint analysis.
• The proposed methodology can expedite the identification of vehicles involved in accidents.
• Further development could enhance the system's robustness in handling diverse and aged paint samples.