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Fired bullet matching candidate retrieval algorithm based on Siamese neural network.

Bai-En Guo1, Yao Shen1,2, Zhi-Fei Zhou3

  • 1School of Criminal Investigation, People's Public Security University of China, Beijing, China.

Journal of Forensic Sciences
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Siamese neural network (SNN) for faster bullet analysis in forensic investigations. The SNN efficiently retrieves similar bullet candidates, significantly improving the speed of firearm identification.

Keywords:
Siamese neural networkautomatic bullet candidate retrievalballisticscriminal trace retrievaldeep learninginstance retrieval

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Machine Learning

Background:

  • Traditional firearm examination using comparison microscopes is inefficient for gun-related crime investigations.
  • Current methods are labor-intensive and time-consuming, delaying critical investigative processes.

Purpose of the Study:

  • To develop and evaluate a Siamese neural network (SNN) for similarity-based retrieval of bullet candidates.
  • To enhance the efficiency and speed of ballistic examinations in forensic investigations.

Main Methods:

  • A dataset of 17,870 bullets from 8,935 firearms was created, with surface topographies captured as 2D images.
  • A Siamese neural network was trained on this dataset for instance retrieval of similar bullet images.
  • The system was tested for its accuracy in retrieving matching bullet candidates from a gallery.

Main Results:

  • The SNN achieved high retrieval accuracies: 80.2% for top 1, 93.4% for top 5, and 97.3% for top 10.
  • This method demonstrated effectiveness even with a large and complex dataset, expanding on previous research.
  • The system successfully expedited the process of narrowing down potential firearm matches.

Conclusions:

  • The SNN-based bullet retrieval system significantly improves the efficiency of ballistic analysis.
  • This technology supports forensic examiners by quickly identifying potential firearm matches, aiding investigations.
  • The study highlights the potential of AI in advancing forensic science capabilities.