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

Kamaruddin, Saadi Bin Ahmad; Ghani, Nor Azura Md; Liong, Choong Yeun; Jemain, Abdul Aziz

doi:10.1109/icpair.2011.5976891

Cited by 7 publications

(3 citation statements)

References 9 publications

(18 reference statements)

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“…Ghani [13] proposed an AFIS that extracts seven selected orthogonal Legendre moments as features for identification from a circular shape ROI of radius , 2 R whereas Kamaruddin et al [8] proposed an AFIS that extracts six selected geometric moments for identification purposes from a circular shape ROI of radius . Ghani [13] proposed an AFIS that extracts seven selected orthogonal Legendre moments as features for identification from a circular shape ROI of radius , 2 R whereas Kamaruddin et al [8] proposed an AFIS that extracts six selected geometric moments for identification purposes from a circular shape ROI of radius .…”

Section: Resultsmentioning

confidence: 99%

“…Hence, several automatic firearm identification systems (AFIS) have been developed for commercial and testing purposes in order to overcome the drawbacks of the traditional approach [1][2][3][4][5][6][7][8][9][10]. The traditional firearm identification approach used in forensic laboratories is based on image matching by using comparison microscopes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient automatic firearm identification system

Chuan

Liong²,

Jemain³

et al. 2014

AIP Conference Proceedings

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Automatic firearm identification system (AFIS) is highly demanded in forensic ballistics to replace the traditional approach which uses comparison microscope and is relatively complex and time consuming. Thus, several AFIS have been developed for commercial and testing purposes. However, those AFIS are still unable to overcome some of the drawbacks of the traditional firearm identification approach. The goal of this study is to introduce another efficient and effective AFIS. A total of 747 firing pin impression images captured from five different pistols of same make and model are used to evaluate the proposed AFIS. It was demonstrated that the proposed AFIS is capable of producing firearm identification accuracy rate of over 95.0% with an execution time of less than 0.35 seconds per image.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient automatic firearm identification system

Chuan

Liong²,

Jemain³

et al. 2014

AIP Conference Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…After feature selection the method achieves a correct classification rate of 96.7%. Another work by Ghani et al [11] is based on the same test set, a selection of six features from [10], and a backpropagation neural network for classification. The best result is a correct classification rate of 96%.…”

Section: Related Workmentioning

confidence: 99%

Automated Firearm Identification

Fischer

Vielhauer

2015

Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security

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The examination of firearm related toolmarks impressed to cartridges and bullets is a well known forensic discipline. The application of three dimensional imaging systems and pattern recognition techniques for automatic comparison and matching of topographic data is a central field of research in the domain of digital crime scene analysis. In this work, we introduce and evaluate a novel Multiple-Slice-Shape (MSS) approach with the objective to closer link the preprocessing and feature extraction stages and improve the automated examinations of firearm toolmark surface data. We employ two existing features which are applied to the topography of firing pin impressions and aim at an automatic matching of the shapes based on multiple line-profile measurement. We suggest several modifications of the original Multiple-Angle-Path (MAP) and Multiple-Circle-Path (MCP) features to achieve an optimal integration into the proposed processing pipeline. Our evaluation approach is three-fold. First, we aim at the determination of an initial parameterization for MSS processing and feature extraction. Second, we evaluate the accuracy of discrimination for two firearms of the same mark and model. Third, we evaluate the accuracy using six different weapons. The test set contains 72 cartridge samples including six guns and three ammunition manufactures. Regarding the first evaluation, the results indicate an improvement of the accuracy for both features. Regarding the second evaluation, the achieved accuracy ranges between 67% and 100% for the MAP feature, and between 92% and 100% for the MCP feature. With respect to the third evaluation, the best result is achieved for MAP 32 with 73% and for MCP 15 with 92% compared to 56% and 82% correct classification rate regarding the original versions. It is supposed that various 3D spatial features can be combined and maybe improved by using the proposed MSS approach. We motivate the evaluation of this question for future work.

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Gun Model Classification Based on Fired Cartridge Case Head Images with Siamese Networks

Valentim

Fonseca

Ferreira

et al. 2022

Intelligent Systems Design and Applications

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The identification of the firearm model that triggered the firing of a bullet is an important forensic information that, historically, has been done by trained examiners through visual inspection using microscopes. This is an extensive and very time-consuming process that requires the examiners to be trained to identify and compare the fired cartridges. This paper proposes an automated objective method for binary classifying pairs of fired cartridge head images as belonging to the same or different classes, using siamese neural networks (SNNs). With this technique, an accuracy of up to 70% was reached by using firing pin mark images as the input of the SNN. For the training and optimization of the network this paper also analyses and presents different image preprocessing approaches.

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

Cited by 7 publications

References 9 publications

An efficient automatic firearm identification system

An efficient automatic firearm identification system

Automated Firearm Identification

Gun Model Classification Based on Fired Cartridge Case Head Images with Siamese Networks

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