Comparison of three similarity scores for bullet LEA matching

VanderPlas, Susan; Nally, Melissa; Klep, Tylor; Cadevall, Cristina; Hofmann, Heike

doi:10.1016/j.forsciint.2020.110167

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…A random forest model fit to an expanded dataset of 83,028 land‐to‐land comparisons from two sets of bullets in the James Hamby Consecutively Rifled Ruger Barrel Study [12] was validated in Vanderplas et al [30]. Here, we train a random forest model to mimic the model validated in Vanderplas et al [30] using the same data, model structure, and features (the nine similarity measures developed in Hare et al [13]). We apply the trained random forest model to 6 bullets from another set of the Hamby study with 364 rows of land comparisons.…”

Section: Application To Bullet Matching Datamentioning

confidence: 99%

Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations

Goode

Hofmann

2021

Statistical Analysis

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The importance of providing explanations for predictions made by black‐box models has led to the development of explainer model methods such as LIME (local interpretable model‐agnostic explanations). LIME uses a surrogate model to explain the relationship between predictor variables and predictions from a black‐box model in a local region around a prediction of interest. However, the quality of the resulting explanations relies on how well the explainer model captures the black‐box model in a specified local region. Here we introduce three visual diagnostics to assess the quality of LIME explanations: (1) explanation scatterplots, (2) assessment metric plots, and (3) feature heatmaps. We apply the visual diagnostics to a forensics bullet matching dataset to show examples where LIME explanations depend on the tuning parameter values and the explainer model oversimplifies the black‐box model. Our examples raise concerns about claims made of LIME that are similar to other criticisms in the literature.

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Section: Application To Bullet Matching Datamentioning

confidence: 99%

Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations

Goode

Hofmann

2021

Statistical Analysis

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“…To date, many traditional bullet identification methods have been developed to help identify unknown firearms, including microscopic detection [8], [15], [16], the continuous shooting method [17], and roughness measurements with a stylus [18]. These identification methods are time consuming and are likely to be subjective [19]- [21].…”

Section: B Related Workmentioning

confidence: 99%

Automated Firearm Classification From Bullet Markings Using Deep Learning

et al. 2020

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Firearm violence is one of the leading causes of death in many countries around the world, including Thailand. This work proposes a fast and accurate automated method to classify firearm brands from bullet markings. Specifically, a panoramic image of a bullet collected from a crime scene was captured using a developed mobile phone application and custom-built portable hardware. The top three state-of-the-art CNNs pretrained on ImageNet-DenseNet121, ResNet50, and Xception-were further trained on the same training set, which was composed of 718 bullets collected from eight different firearm brands-Beretta, Browning, CZ, Glock, Norinco, Ruger, Sig Sauer, and Smith & Wesson-using a five-fold cross validation technique. DenseNet121 provided the highest AUC of 0.99 for CZ classification (the most common registered firearm brand in Thailand) and the highest average AUC for the eight firearm brands (0.9780 ± 0.0130 SD), which was significantly higher than those of ResNet50 and Xception. In addition, there were no interaction effects between the CNN model and firearm brand on AUC. DenseNet121, which had the highest AUC, was evaluated on the test set (72 bullets), and the results showed that the Beretta and CZ classifications had the lowest accuracy (91.18%), followed by the Browning and Norinco classifications (96.88%), whereas the Glock, Ruger, Sig Sauer, and Smith & Wesson classifications had the highest accuracy (98.41%). These results suggest that the developed mobile phone application based on a deep learning algorithm and the custom-built portable hardware have promising potential for use at crime scenes to classify firearms from bullet markings. By narrowing down the list of suspects, this convenient approach can potentially accelerate bullet identification processes for many forensic science examiners. INDEX TERMS Forensic science, automated firearm classification, 9 mm bullet marking, densely connected convolutional network.

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“…During the last two decades, new innovative 3D surface topological scanning microscopy has been developed with the potential to improve physical matching. Different 3D acquisition systems, employing 3D laser scanners, optical coherence tomography, stylus scanning instruments, and confocal microscopy, have been utilized for forensic evidence identification applications [19,26,29‐43]. Automated surface acquisition and matching processes utilizing 3D topography data have demonstrated promising improvements in the objectivity of the comparison process [34].…”

Section: Introductionmentioning

confidence: 99%

“…During the last two decades, new innovative 3D surface topological scanning microscopy has been developed with the potential to improve physical matching. Different 3D acquisition systems, employing 3D laser scanners, optical coherence tomography, stylus scanning instruments, and confocal microscopy, have been utilized for forensic evidence identification applications [19,26,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Quantitative matching of forensic evidence fragments utilizing 3D microscopy analysis of fracture surface replicas

Dawood

Llosa-Vite

Thompson

et al. 2022

Journal of Forensic Sciences

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Silicone casts are widely used by practitioners in the comparative analysis of forensic items. Fractured surfaces carry unique details that can provide accurate quantitative comparisons of forensic fragments. In this study, a statistical analysis comparison protocol was applied to a set of 3D topological images of fractured surface pairs and their replicas to provide confidence in the quantitative statistical comparison between fractured items and their silicone cast replicas. A set of 10 fractured stainless steel samples were fractured from the same metal rod under controlled conditions and were replicated using a standard forensic casting technique. Six 3D topological maps with 50% overlap were acquired for each fractured pair. Spectral analyses were utilized to identify the correlation between topological surface features at different length scales of the surface topology. We selected two frequency bands over the critical wavelength (greater than two‐grain diameters) for statistical comparison. Our statistical model utilized a matrix‐variate t‐distribution that accounts for overlap between images to model match and non‐match population densities. A decision rule identified the probability of matched and unmatched pairs of surfaces. The proposed methodology correctly classified the fractured steel surfaces and their replicas with a posterior probability of match exceeding 99.96%. Moreover, the replication technique shows potential in accurately replicating fracture surface topological details with a wavelength greater than 20 μm, which far exceeds the feature comparison range on most metallic alloy surfaces. Our framework establishes the basis and limits for forensic comparison of fractured articles and their replicas while providing a reliable fracture mechanics‐based quantitative statistical forensic comparison.

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Comparison of three similarity scores for bullet LEA matching

Cited by 14 publications

References 20 publications

Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations

Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations

Automated Firearm Classification From Bullet Markings Using Deep Learning

Quantitative matching of forensic evidence fragments utilizing 3D microscopy analysis of fracture surface replicas

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