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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers ( IEEE )
2011
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| 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 |
| Summary: | 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. |
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