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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848820863102615552 |
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| author | Z. L., Chuan A. A., Jemain C-Y, Liong N. A. M., Ghani L. K., Tan |
| author_facet | Z. L., Chuan A. A., Jemain C-Y, Liong N. A. M., Ghani L. K., Tan |
| author_sort | Z. L., Chuan |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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%. |
| first_indexed | 2025-11-15T02:16:12Z |
| format | Conference or Workshop Item |
| id | ump-19255 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:16:12Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-192552022-01-17T01:49:37Z http://umpir.ump.edu.my/id/eprint/19255/ A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens Z. L., Chuan A. A., Jemain C-Y, Liong N. A. M., Ghani L. K., 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 Z. L., Chuan and A. A., Jemain and C-Y, Liong and N. A. M., Ghani and L. K., 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) (Published) https://doi.org/10.1088/1742-6596/890/1/012126 |
| spellingShingle | QA75 Electronic computers. Computer science Z. L., Chuan A. A., Jemain C-Y, Liong N. A. M., Ghani L. K., Tan A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens |
| title | 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_short | A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens |
| title_sort | robust firearm identification algorithm of forensic ballistics specimens |
| topic | QA75 Electronic computers. Computer science |
| 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 |