Firearm identification based on rotation invariant feature of cartridge case

Thumwarin, P.; Prasit, C.; Matsuura, T.

doi:10.1109/sice.2008.4654620

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…The results are presented in a subjective fashion. An extension of this work is introduced by Thumwarin et al [8]. The approach is expanded by using a Wiener filter for handling noise and fluctuation.…”

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%

“…If a point ∈ is located inside : Two examples of the corresponding circle window algorithm and the estimated corresponding points using eight ( 8) and sixteen angles ( 16) are shown in Figure 11. Please note, that all lines are emphasized to increase the visibility.…”

Section: IIImentioning

confidence: 99%

Automated Firearm Identification

Fischer

Vielhauer

2015

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

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“…Comparisons of these toolmarks have more than a hundred years of history through the use of manual comparison microscopes [ 1 ]. In recent years, automated identification technologies and systems have been developed [ 2 , 3 ]. For correlations of the cartridge case, most identification systems correlate the whole region without taking into account the fact that not all regions on the primer are well-marked by the impression of the breech face of the gun slide during firing.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications

Tong

Song

Chu

2015

J. RES. NATL. INST. STAN.

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The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for firearm evidence identifications. The CMC method divides the measured image of a surface area, such as a breech face impression from a fired cartridge case, into small correlation cells and uses four identification parameters to identify correlated cell pairs originating from the same firearm. The CMC method was validated by identification tests using both 3D topography images and optical images captured from breech face impressions of 40 cartridge cases fired from a pistol with 10 consecutively manufactured slides. In this paper, we discuss the processing of the cell correlations and propose an improved algorithm of the CMC method which takes advantage of the cell correlations at a common initial phase angle and combines the forward and backward correlations to improve the identification capability. The improved algorithm is tested by 780 pairwise correlations using the same optical images and 3D topography images as the initial validation.

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“…In recent years, researchers and forensic scientists have been studying and improving automatic forensic identification systems. For cartridge cases, many ballistic evidence search methods compare whole breech face images to make correlations [ 1 , 2 ]. But in many cases, not all areas of the breech face impression are related to the breech face topography of gun slide.…”

Section: Introductionmentioning

confidence: 99%

Fired Cartridge Case Identification Using Optical Images and the Congruent Matching Cells (CMC) Method

Tong¹,

Song²,

Chu³

et al. 2014

J. RES. NATL. INST. STAN.

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The Congruent Matching Cells (CMC) method for ballistics identification was invented at the National Institute of Standards and Technology (NIST). The CMC method is based on the correlation of pairs of small correlation cells instead of the correlation of entire images. Four identification parameters – TCCF, Tθ, Tx and Ty are proposed for identifying correlated cell pairs originating from the same firearm. The correlation conclusion (matching or non-matching) is determined by whether the number of CMC is ≥ 6. This method has been previously validated using a set of 780 pair-wise 3D topography images. However, most ballistic images stored in current local and national databases are in an optical intensity (grayscale) format. As a result, the reliability of applying the CMC method on optical intensity images is an important issue. In this paper, optical intensity images of breech face impressions captured on the same set of 40 cartridge cases are correlated and analyzed for the validation test of CMC method using optical images. This includes correlations of 63 pairs of matching images and 717 pairs of non-matching images under top ring lighting. Tests of the method do not produce any false identification (false positive) or false exclusion (false negative) results, which support the CMC method and the proposed identification criterion, C = 6, for firearm breech face identifications using optical intensity images.

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Firearm identification based on rotation invariant feature of cartridge case

Cited by 8 publications

References 1 publication

Automated Firearm Identification

Automated Firearm Identification

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications

Fired Cartridge Case Identification Using Optical Images and the Congruent Matching Cells (CMC) Method

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