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Classification of firing pin impressions using HOG-SVM.

Zhijian Wen1, James M Curran1, SallyAnn Harbison1,2

  • 1Institute of Environmental Science and Research Limited, Auckland, New Zealand.

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Firearm examination uses computer vision and machine learning to analyze bullet marks. This method accurately links cartridge cases to specific firearms, aiding criminal investigations.

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Machine Learning

Background:

  • Firearm examination is crucial for criminal investigations, particularly in linking cartridge cases to suspect firearms.
  • Firing pin impressions on cartridge cases are key identifying marks used in forensic analysis.
  • Traditional methods rely on manual comparison, which can be time-consuming and subjective.

Purpose of the Study:

  • To develop and evaluate a computational method for classifying firing pin impressions.
  • To assess the accuracy of a computer vision algorithm (Histogram of Oriented Gradients - HOG) combined with a machine learning method (Support Vector Machines - SVMs) for this task.
  • To compare the performance of the HOG-SVM method against other feature extraction algorithms.

Main Methods:

  • Nine Ruger model 10/22 semiautomatic rifles were used, firing 50 cartridges each.
  • Cartridge cases were collected, and firing pin impressions were cast and photographed.
  • Images of firing pin impressions were analyzed using the Histogram of Oriented Gradients (HOG) algorithm and Support Vector Machines (SVMs).

Main Results:

  • The developed HOG-SVM method achieved a classification accuracy of 93% for firing pin impressions.
  • The HOG-SVM method demonstrated superior performance compared to other feature extraction algorithms tested.
  • The study successfully demonstrated the potential of computational methods in firearm examination.

Conclusions:

  • The HOG-SVM approach offers a reliable and accurate computational tool for classifying firing pin impressions.
  • This method can significantly assist firearm examiners in associating cartridge cases with specific firearms at crime scenes.
  • Automated analysis of ballistic evidence holds promise for enhancing the efficiency and objectivity of forensic investigations.