Conference Paper

Analysis of Geometric Moments as Features for Identification of Forensic Ballistics Specimen.

DOI: 10.1007/978-3-642-02481-8_88 Conference: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II
Source: DBLP

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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