Analysis of geometric moments as features for firearm identification

Ghani, Nor Azura Md; Liong, Choong Yeun; Jemain, Abdul Aziz

doi:10.1016/j.forsciint.2010.02.011

Cited by 22 publications

(21 citation statements)

References 5 publications

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“…Ghani et al [10] introduce the application of different order geometric moments for automated firearm identification. Based on five different weapons, 747 intensity images of firing pin impressions are used as test data and 48 features are suggested.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…The best result is a correct classification rate of 96%. Two later publications [12,13] of the authors describe the use of the same test set and different subsets of features from the original feature set [10]. The utilization of 16 features based on firing pin center images in [12] results in a correct classification rate of 75.4%.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Automated Firearm Identification

Fischer

Vielhauer

2015

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

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“…For example, Geradts et al [7] manually selected the primer area and removed the rest. In [13], the primer was extracted and then further segmented as firing pin impression area from the rest of the primer; however, it is not clear how this is done. In [14], the CC image was isolated from the background by using colour invariants and Hough transform-based circle detection.…”

Section: Related Workmentioning

confidence: 99%

Characterisation of tool marks on cartridge cases by combining multiple images

Leloglu

2012

IET Image Process.

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The characteristic marks left by firearms on cartridge cases (CCs) during firing are used by forensic experts to identify CCs fired from the same firearm; however, the nature of the tool marks on the CCs is not well understood. The objective of this study is to separate the tool marks i.e., the signal, from the background signal and the noise and thereby understand its peculiarities. To extract the signal, which is much weaker than the noise, second-order derivatives of three-dimensional images of the surfaces of a series of CCs fired from the same firearm were used. Instead of using rigid body transformation, the images are first registered based on estimated planar homographies; then unwanted areas, including the headstamp are masked out. Aligned images are merged by weighted averaging using estimated signal and noise variances, which are also used for calculating signal and noise spectra. Wiener filtering is applied to further increase the signal-to-noise ratio. The effectiveness of the method is demonstrated on real data. It is also shown that breech face marks exist on the outer ring, a feature typically ignored in automatic matching. In addition, visual results obtained by reconstructing the surface from second-order derivatives are presented.

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“…This has given a means of analysing features automatically containing such a firearm fingerprint, the type and model of a firearm, as well as attributes of each individual weapon as successfully as human fingerprints. Law enforcement agencies such as Royal Malaysian Police (RMP) expressed considerable interest in the use of ballistics imaging recognition systems to both greatly reduce the time for a positive identification and henceforth introduce reliabilityrepeatability to the process [4]- [6]. Consequently, the cataloguing of firearms from selected numeric based features of firing pin impression image using backpropagation neural network is proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

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

Kamaruddin

Ghani

Liong

et al. 2011

2011 International Conference on Pattern Analysis and Intelligence Robotics

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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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Analysis of geometric moments as features for firearm identification

Cited by 22 publications

References 5 publications

Automated Firearm Identification

Automated Firearm Identification

Characterisation of tool marks on cartridge cases by combining multiple images

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

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