Citation

Bigdeli S, Ebrahimi Moghaddam M. J. Forensic Sci. 2019; 64(3): 741-753.

Affiliation

Faculty of Computer Science and Engineering, Shahid Beheshti University, Evin Ave, Tehran, Iran, 1983969411.

Copyright

(Copyright © 2019, American Society for Testing and Materials, Publisher John Wiley and Sons)

DOI

10.1111/1556-4029.13956

PMID

30462835

Abstract

In the field of forensic science, bullet identification is based on the fact that firing the cartridge from a barrel leaves exclusive microscopic striation on the fired bullets as the fingerprint of the firearm. The bullet identification methods are categorized in 2-D and 3-D based on their image acquisition techniques. In this study, we focus on 2-D optical images using a multimodal technique and propose several distinct methods as its modalities. The proposed method uses a multimodal rule-based linear weighted fusion approach which combines the semantic level decisions from different modalities with a linear technique that its optimized modalities weights have been identified by the genetic algorithm. The proposed approach was applied on a dataset, which includes 180 2-D bullet images fired from 90 different AK-47 barrels. The experimentations showed that our approach attained better results compared to common methods in the field of bullet identification.

© 2018 American Academy of Forensic Sciences.

Language: en

Keywords

automatic bullet identification; automatic firearm identification; cross-correlation function; ensemble empirical mode decomposition; forensic science; multimodal fusion