Correlation of firing pin impressions based on congruent matching cross-sections (CMX) method

Automated interpretation of comparison scores for firearm toolmarks on cartridge case primers

An automated approach for evaluating the strength of the evidence of firearm toolmark comparison results is presented for a common source scenario. First, comparison scores are derived describing the similarity of marks typically encountered on the primer of fired cartridge cases: aperture shear striations as well as breechface and firing pin impressions. Subsequently, these scores are interpreted using reference distributions of comparison scores obtained for representative known matching (KM) and known non-matching (KNM) ballistic samples in a common source, score-based likelihood ratio (LR) system. We study various alternatives to set up such an LR system and compare them using qualitative and quantitative criteria known from the literature. As an example, results are applied to establish a system suitable for a firearm-ammunition combination often encountered in casework: Glock firearms with Fiocchi nickel primer ammunition. The system outputs an LR and a measure of LR uncertainty. The range of possible LR-values is limited to a minimum and maximum value in areas of the score domain with little reference data. Finally, the feasibility of combining LRs of different mark types into one LR for the entire primer is assessed. For the distribution models considered in this paper, different modeling approaches are optimal for different types of similarity scores. For the chosen firearm-ammunition combination, non-parametric Kernel Density Estimation (KDE) models perform best for similarity scores based on the correlation coefficient, whereas parametric models perform best for the Congruent Matching Cells (CMC) scores, assuming binomial and beta-binomial models for KM and KNM score distributions respectively. Finally, it is demonstrated that individual LRs of different mark types can be combined into one LR, to interpret a set of different marks on the primer as a whole.

Forensic comparison of fired cartridge cases: Feature-extraction methods for feature-based calculation of likelihood ratios

We describe and validate a feature-based system for calculation of likelihood ratios from 3D digital images of fired cartridge cases. The system includes a database of 3D digital images of the bases of 10 cartridges fired per firearm from approximately 300 firearms of the same class (semi-automatic pistols that fire 9 mm diameter centre-fire Luger-type ammunition, and that have hemispherical firing pins and parallel breech-face marks). The images were captured using Evofinder®, an imaging system that is commonly used by operational forensic laboratories. A key component of the research reported is the comparison of different feature-extraction methods. Feature sets compared include those previously proposed in the literature, plus Zernike-moment based features. Comparisons are also made of using feature sets extracted from the firing-pin impression, from the breech-face region, and from the whole region of interest (firing-pin impression + breech-face region + flowback if present). Likelihood ratios are calculated using a statistical modelling pipeline that is standard in forensic voice comparison. Validation is conducted and results are assessed using validation procedures and validation metrics and graphics that are standard in forensic voice comparison.

Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods

When a bullet is fired from a barrel, micro striation marks caused by the sliding motion of the bullet through the rifled barrel are one of the foremost factors in automated ballistic identification. This paper focuses on 3D topography images of land engraved areas (LEA) and proposes a bullet identification method incorporating the finite ridgelet transform (FRIT) and gray level co-occurrence matrix (GLCM) algorithms. The FRIT extracts the striation marks from the 3D micro image and the GLCM generates a linearly weighted weight corresponding to the texture features for 2D average profile calculation. The entire striation marks image is divided into several cells and a cell with valid correlation areas is assigned a large weight, but the one with invalid correlation areas is assigned a small weight along the vertical direction. The visible results show that the valid correlation areas are effectively identified and the negative effects of invalid correlation areas are suppressed. Tests were performed on a control set and an unknown set, giving a total of 35 bullet samples fired from pistols with 10 consecutively manufactured slides. The results included no false identifications or false exclusions and a clear separation between the matching index of the matching and non-matching LEA profiles, demonstrating excellent performance in striation mark capture and valid correlation areas extraction of FRIT and GLCM algorithms. The proposed method is capable of correctly matching toolmarked surfaces to the barrel used.

Convergence-improved congruent matching cells (CMC) method for firing pin impression comparison

A firing pin impression is usually concave in shape with a small textured area, which makes it difficult to perform automated algorithm-based comparison. The congruent matching cells (CMC) method was invented for accurate breech face impression comparison, in which a reference impression is divided into correlation cells. Each cell is registered to a cell-sized area of the comparison impression that has maximum similarity in surface topography. Four parameters are used to quantify the congruent matching pattern of the registration position and orientation. This paper aims to further develop the cell-division-matching method based on a convergence feature and to develop practical convergence-improved algorithms for firing pin impression comparison. The convergence feature refers to the tendency of the x-y registration positions of correlated cell pairs to converge at the correct registration angle when comparing same-source samples at different orientations. The areal Gaussian filter is employed to extract high-frequency micro-features; the least-squares matching method is used to improve each cross-correlation precision and reach convergence in the registration positions of correlated cell pairs; and a density-based clustering algorithm is introduced to collect the registration positions of dense cell pairs relative to a virtual common center and to remove outliers. Improvements are achieved in the reliability and accuracy of the number of congruent matching cell pairs (CMCs) collected, which represents the quantification of the degree of pairwise impression similarity. Experiments in this report used 40 firing pin impression samples on cartridge cases fired from 10 pistols. The results included no false identifications or false exclusions.

Interpol review of forensic firearm examination 2016-2019

This review paper covers the relevant literature on forensic firearm examination from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

Pilot study of feature-based algorithm for breech face comparison

A novel feature-based method, which is scale invariant feature transform (SIFT) and RANdom SAmple Consensus (RANSAC) integration algorithm, is introduced to promote the automated identification of the breech face impression, the most common mark left on the cartridge used for firearm evidence. SIFT algorithm is employed to extract the local extrema from examined impression as keypoints representing its invariant features, and to build the feature descriptor for each keypoint based on its local gradients in neighborhood. RANSAC is used to improve the matching performance among these keypoints and feature descriptors. With hypothesize-and-verify methods, RANSAC is able to construct the best model fitting initial matching pairs of keypoints and to guarantee the robust comparison result. Validation tests using 40 cartridge cases fired from pistols with 10 consecutively manufactured slides yielded a clear separation result, which strongly supports the effectiveness of the ensemble algorithm of SIFT and RANSAC. This application indicates the practical feasibility of feature-based algorithm and image processing technique in forensic science.

Estimating error rates for firearm evidence identifications in forensic science

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence.