Firearm recognition based on whole firing pin impression image via backpropagation neural network

Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. In this study, a set of data which focused on selected 6 features of firing pin impression images befor...

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Main Authors: Ahmad Kamaruddin, Saadi, Md Ghani, Nor Azura, Liong, Choong-Yeun, Jemain, Abdul Aziz
Other Authors: Omar, K
Format: Book Chapter
Language:English
Published: Institute of Electrical and Electronics Engineers ( IEEE ) 2011
Subjects:
Online Access:http://irep.iium.edu.my/12994/
http://irep.iium.edu.my/12994/
http://irep.iium.edu.my/12994/2/Firearm_Recognition_based_on_Whole_Firing_Pin.pdf
id iium-12994
recordtype eprints
spelling iium-129942017-06-19T02:02:30Z http://irep.iium.edu.my/12994/ Firearm recognition based on whole firing pin impression image via backpropagation neural network Ahmad Kamaruddin, Saadi Md Ghani, Nor Azura Liong, Choong-Yeun Jemain, Abdul Aziz QA75 Electronic computers. Computer science Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. In this study, a set of data which focused on selected 6 features of firing pin impression images before an entirety of five different pistols of South African made; the Parabellum Vector SPI 9mm model, were used. The numerical features are geometric moments of whole image computed from a total of 747 cartridge case images. Under pattern recognition theory, the supervised features of firing pin impression images were then trained and validated using a two-layer backpropagation neural network (BPNN) design with computed hidden layers. A two-layer 6-7-5 connections BPNN of sigmoid/linear transfer functions with ‘trainlm’ algorithm was found to yield the best classification result using cross-validation, where 96% of the images were correctly classified according to the pistols used. Moreover, the network was trained under very small mean-square error (MSE=0.01). This means that neural network method is capable to learn and validate well the numerical features of whole firing pin impression with high precision and fast classification results. Institute of Electrical and Electronics Engineers ( IEEE ) Omar, K 2011-06-28 Book Chapter PeerReviewed application/pdf en cc_by_nc_sa http://irep.iium.edu.my/12994/2/Firearm_Recognition_based_on_Whole_Firing_Pin.pdf Ahmad Kamaruddin, Saadi and Md Ghani, Nor Azura and Liong, Choong-Yeun and Jemain, Abdul Aziz (2011) Firearm recognition based on whole firing pin impression image via backpropagation neural network. In: 2011 International Conference on Pattern Analysis and Intelligence Robotics (ICPAIR 2011). Institute of Electrical and Electronics Engineers ( IEEE ) , Kuala Lumpur, Malaysia, pp. 117-182. ISBN 9781612844077 10.1109/ICPAIR.2011.5976891
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmad Kamaruddin, Saadi
Md Ghani, Nor Azura
Liong, Choong-Yeun
Jemain, Abdul Aziz
Firearm recognition based on whole firing pin impression image via backpropagation neural network
description Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. In this study, a set of data which focused on selected 6 features of firing pin impression images before an entirety of five different pistols of South African made; the Parabellum Vector SPI 9mm model, were used. The numerical features are geometric moments of whole image computed from a total of 747 cartridge case images. Under pattern recognition theory, the supervised features of firing pin impression images were then trained and validated using a two-layer backpropagation neural network (BPNN) design with computed hidden layers. A two-layer 6-7-5 connections BPNN of sigmoid/linear transfer functions with ‘trainlm’ algorithm was found to yield the best classification result using cross-validation, where 96% of the images were correctly classified according to the pistols used. Moreover, the network was trained under very small mean-square error (MSE=0.01). This means that neural network method is capable to learn and validate well the numerical features of whole firing pin impression with high precision and fast classification results.
author2 Omar, K
author_facet Omar, K
Ahmad Kamaruddin, Saadi
Md Ghani, Nor Azura
Liong, Choong-Yeun
Jemain, Abdul Aziz
format Book Chapter
author Ahmad Kamaruddin, Saadi
Md Ghani, Nor Azura
Liong, Choong-Yeun
Jemain, Abdul Aziz
author_sort Ahmad Kamaruddin, Saadi
title Firearm recognition based on whole firing pin impression image via backpropagation neural network
title_short Firearm recognition based on whole firing pin impression image via backpropagation neural network
title_full Firearm recognition based on whole firing pin impression image via backpropagation neural network
title_fullStr Firearm recognition based on whole firing pin impression image via backpropagation neural network
title_full_unstemmed Firearm recognition based on whole firing pin impression image via backpropagation neural network
title_sort firearm recognition based on whole firing pin impression image via backpropagation neural network
publisher Institute of Electrical and Electronics Engineers ( IEEE )
publishDate 2011
url http://irep.iium.edu.my/12994/
http://irep.iium.edu.my/12994/
http://irep.iium.edu.my/12994/2/Firearm_Recognition_based_on_Whole_Firing_Pin.pdf
first_indexed 2023-09-18T20:22:07Z
last_indexed 2023-09-18T20:22:07Z
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