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

Authors:
Nor Azura Ghani;Choong-Yeun Liong;Abdul Aziz Jemain
Affiliations:
Center for Statistical Studies, Faculty of Information Technology & Quantitative Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia 40450;School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Malaysia 43600;School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Malaysia 43600
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

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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.