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A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens

Chuan, Zun Liang and Abdul Aziz, Jemain and Choong, Yeun Liong and Nor Azura, Md Ghani and Lit, Ken Tan (2017) A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens. In: Journal of Physics: Conference Series, 1st International Conference on Applied & Industrial Mathematics and Statistics 2017 (ICoAIMS 2017), 8-10 August 2017 , Kuantan, Pahang, Malaysia. pp. 1-10., 890 (012126). ISSN 1742-6588 (print); 1742-6596 (online)

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Abstract

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Ballistics; Image processing; Image segmentation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Dr. Zun Liang Chuan
Date Deposited: 07 Dec 2017 03:12
Last Modified: 07 Dec 2017 03:14
URI: http://umpir.ump.edu.my/id/eprint/19255
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