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A correlation based bullet identification method using empirical mode decomposition.

Saeed Bigdeli1, Hamed Danandeh1, Mohsen Ebrahimi Moghaddam1

  • 1Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran.

Forensic Science International
|August 15, 2017
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Summary
This summary is machine-generated.

This study introduces a new method using ensemble empirical mode decomposition (EEMD) for firearm identification. EEMD effectively preprocesses bullet striation images, improving accuracy in identifying unique firearm markings.

Keywords:
Automatic bullet identificationCross correlationEnsemble Empirical mode decomposition

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Area of Science:

  • Forensic Science
  • Image Processing
  • Mechanical Engineering

Background:

  • Bullet striations are unique 3D microstructures crucial for firearm identification.
  • Current methods using linear time-invariant (LTI) filters struggle with nonlinear baseline drifts and information loss.
  • Bullet striation patterns are statistically non-stationary due to random imperfections in the rifling process.

Purpose of the Study:

  • To propose a novel preprocessing method for bullet image analysis in firearm identification.
  • To address the limitations of LTI filters in handling nonlinear and non-stationary bullet striation data.
  • To enhance the robustness and efficiency of automatic firearm identification systems.

Main Methods:

  • Bullet images are treated as nonlinear, non-stationary processes.
  • Ensemble Empirical Mode Decomposition (EEMD) is employed as a preprocessing algorithm.
  • EEMD is used for smoothing and feature extraction from bullet surface striations.

Main Results:

  • The proposed EEMD method effectively reduces noise in bullet images.
  • Features extracted using EEMD are free from nonlinear baseline drifts.
  • The method demonstrated superior performance compared to two common techniques in experimental evaluations.

Conclusions:

  • EEMD offers a robust and efficient preprocessing solution for analyzing bullet striations.
  • This approach significantly improves the accuracy and reliability of automatic firearm identification.
  • The findings suggest a promising direction for advancing forensic ballistics analysis.