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A Multimodal Fusion Approach for Bullet Identification Systems
Author(s) -
Bigdeli Saeed,
Ebrahimi Moghaddam Mohsen
Publication year - 2019
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13956
Subject(s) - identification (biology) , modalities , computer science , artificial intelligence , field (mathematics) , fingerprint (computing) , image fusion , computer vision , pattern recognition (psychology) , machine learning , image (mathematics) , mathematics , social science , botany , sociology , pure mathematics , biology
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.