Abstract
Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm, regardless of its size, make and model, has its own unique ‘fingerprint’ with respect to the marks on fired bullet and cartridge cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of 747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six features. Classification results using cross-validation show that about 74.0% of the images were correctly classified and this demonstrates the potential of using moment based features for firearm identification.
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Ghani, N.A.M., Liong, CY., Jemain, A.A. (2009). Analysis of Geometric Moments as Features for Identification of Forensic Ballistics Specimen. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_88
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DOI: https://doi.org/10.1007/978-3-642-02481-8_88
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