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

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 Intelligent Robotics, ICPAIR 2011, 28-29 June 2011, Putrajaya, Malaysia.

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Abstract

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 function 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.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	6666/16193
Uncontrolled Keywords:	forensic ballistics, firearm identification, firearm analysis, geometric moment, backpropagation neural network (BPNN)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science > Department of Computational and Theoretical Sciences
Depositing User:	Ms Rosyidah Taju Rahim
Date Deposited:	16 Jan 2012 15:47
Last Modified:	19 Jun 2017 10:17
URI:	http://irep.iium.edu.my/id/eprint/16193

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