A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens

A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens

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 feature...

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Bibliographic Details
Main Authors: Chuan, Zun Liang, Abdul Aziz, Jemain, Choong, Yeun Liong, Nor Azura, Md Ghani, Lit, Ken Tan
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/19255/
http://umpir.ump.edu.my/id/eprint/19255/
http://umpir.ump.edu.my/id/eprint/19255/1/A%20robust%20firearm%20identification%20algorithm%20of%20forensic%20ballistics%20specimens.pdf
id ump-19255
recordtype eprints
spelling ump-192552017-12-07T03:14:58Z http://umpir.ump.edu.my/id/eprint/19255/ A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens Chuan, Zun Liang Abdul Aziz, Jemain Choong, Yeun Liong Nor Azura, Md Ghani Lit, Ken Tan QA75 Electronic computers. Computer science 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%. IOP Publishing 2017 Conference or Workshop Item PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/19255/1/A%20robust%20firearm%20identification%20algorithm%20of%20forensic%20ballistics%20specimens.pdf 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) https://doi.org/10.1088/1742-6596/890/1/012126
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Chuan, Zun Liang
Abdul Aziz, Jemain
Choong, Yeun Liong
Nor Azura, Md Ghani
Lit, Ken Tan
A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
description 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%.
format Conference or Workshop Item
author Chuan, Zun Liang
Abdul Aziz, Jemain
Choong, Yeun Liong
Nor Azura, Md Ghani
Lit, Ken Tan
author_facet Chuan, Zun Liang
Abdul Aziz, Jemain
Choong, Yeun Liong
Nor Azura, Md Ghani
Lit, Ken Tan
author_sort Chuan, Zun Liang
title A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
title_short A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
title_full A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
title_fullStr A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
title_full_unstemmed A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
title_sort robust firearm identification algorithm of forensic ballistics specimens
publisher IOP Publishing
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/19255/
http://umpir.ump.edu.my/id/eprint/19255/
http://umpir.ump.edu.my/id/eprint/19255/1/A%20robust%20firearm%20identification%20algorithm%20of%20forensic%20ballistics%20specimens.pdf
first_indexed 2023-09-18T22:27:36Z
last_indexed 2023-09-18T22:27:36Z
_version_ 1777416071187267584

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