Topography measurements and applications in ballistics and tool mark identifications

Identification of bullets fired from air guns using machine and deep learning methods

Ballistics (the linkage of bullets and cartridge cases to weapons) is a common type of evidence encountered in criminal cases around the world. The interest lies in determining whether two bullets were fired using the same firearm. This paper proposes an automated method to classify bullets from surface topography and Land Engraved Area (LEA) images of the fired pellets using machine and deep learning methods. The curvature of the surface topography was removed using loess fit and features were extracted using Empirical Mode Decomposition (EMD) followed by various entropy measures. The informative features were identified using minimum Redundancy Maximum Relevance (mRMR), finally the classification was performed using Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) classifiers. The results revealed a good predictive performance. In addition, the deep learning model DenseNet121 was used to classify the LEA images. DenseNet121 provided a higher predictive performance than SVM, DT and RF classifiers. Moreover, the Grad-CAM technique was used to visualise the discriminative regions in the LEA images. These results suggest that the proposed deep learning method can be used to expedite the linkage of projectiles to firearms and assist in ballistic examinations. In this work, the bullets that were compared were air pellets fired from both air rifles and a high velocity air pistol. Air guns were used to collect the data because they were more accessible than other firearms and could be used as a proxy, delivering comparable LEAs. The methods developed here can be used as a proof-of-concept and are easily expandable to bullet and cartridge case identification from any weapon.

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

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.

The validation of 3D virtual comparison microscopy (VCM) in the comparison of expended cartridge cases

In this study, the Cadre TopMatch-3D scanner and associated virtual comparison software was evaluated to determine whether this technique is a valid and reliable method of conducting cartridge component comparisons. That is, if virtual comparison microscopy (VCM) produces results at least equivalent to those generated through traditional light comparison microscopy (LCM), the method would be deemed valid for use in an operational setting. Particular emphasis was placed on the capability to render same source conclusions. Of the 40 true identifications available to each examiner, corresponding to a total of 520 comparisons, positive identifications were made more frequently using VCM as compared to traditional LCM where inconclusive conclusions were provided at a higher rate. VCM produced a higher sensitivity (88.41%) and specificity (13.64%) rate than LCM, 80.08% and 12.50%, respectively. Based on the findings of the study, considered together with the benefits that VCM offers in the name of efficiency, it became apparent that Cadre's 3D scanning microscope and its associated virtual comparison software tested in this study is an appropriate and valid technique for conducting comparisons of expended cartridge cases and can be implemented into routine casework for that purpose.

Evaluating firearm examiner conclusion variability using cartridge case reproductions

The forensic science pattern comparison areas, including fingerprints, footwear, and firearms, have been criticized for their subjective nature. While much research has attempted to move these disciplines to more objective methods, examiners are still coming to conclusions based on their own training and experience. To complement this subjectivity, black box studies are necessary to establish the accuracy of these feature-comparison methods. However, when cartridges are fired by a firearm to create cartridge case test sets there may be significant variability within the resulting impressions. This can result in different participants receiving test sets with varying levels of difficulty based on differences in impression quality. Therefore, comparison of accuracy between examiners is not straightforward. To compare accuracy between examiners, a method called double-casting was used to create plastic cartridge case reproductions. Double-casts of twenty-one test sets of master cartridge cases were created and mailed to firearm examiners. The double-casts ensured that all participants were comparing exhibits with the same level of detail. The examiners were tasked with determining if the unknown cartridge case in each set was fired by the same firearm as the three knowns. Automated comparisons were also used to compare the cartridge cases within each set. The results from this study showed that there are differences in examiner conclusions when examining the same evidence. Furthermore, it was shown that automated comparison metrics would benefit examiners as a quality control measure to correct any potential errors and strengthen conclusions.

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.

Results of the 3D Virtual Comparison Microscopy Error Rate (VCMER) Study for firearm forensics

The digital examination of scanned or measured 3D surface topography is referred to as Virtual Comparison Microscopy (VCM). Within the discipline of firearm and toolmark examination, VCM enables review and comparison of microscopic toolmarks on fired ammunition components. In the coming years, this technique may supplement and potentially replace the light comparison microscope as the primary instrument used for firearm and toolmark examination. This paper describes a VCM error rate and validation study involving 107 participants. The study included 40 test sets of fired cartridge cases from firearms with a variety of makes, models, and calibers. Participants used commercially available VCM software which allowed digital data distribution, specimen visualization, and submission of conclusions. The software also allowed participants to annotate areas of similarity and dissimilarity to support their conclusions. The primary cohort of 76 qualified United States and Canadian examiners that completed the study had an overall false-positive error rate of 3 errors from 693 comparisons (0.43%) and a false-negative error rate of 0 errors from 491 comparisons (0.0%). This accuracy is supplemented by the participant's provided surface annotations which provide insight into the cause of errors and the overall consistency across the independent examinations conducted in the study. The ability to obtain highly accurate conclusions on test fires from a wide range of firearms supports the hypothesis that VCM is a useful tool within the crime laboratory.

Firearm examination: Examiner judgments and computer-based comparisons

Forensic firearm examination provides the court of law with information about the source of fired cartridge cases. We assessed the validity of source decisions of a computer-based method and of 73 firearm examiners who compared breechface and firing pin impressions of 48 comparison sets. We also compared the computer-based method's comparison scores with the examiners' degree-of-support judgments and assessed the validity of the latter. The true-positive rate (sensitivity) and true-negative rate (specificity) of the computer-based method (for the comparison of both the breechface and firing pin impressions) were 94.4% and at least 91.7%, respectively. For the examiners, the true-positive rate was at least 95.3% and the true-negative rate was at least 86.2%. The validity of the source decisions improved when the evaluations of breechface and firing pin impressions were combined and for the examiners also when the perceived difficulty of the comparison decreased. The examiners were reluctant to provide source decisions for "difficult" comparisons even though their source decisions were mostly correct. The correlation between the computer-based method's comparison scores and the examiners' degree-of-support judgments was low for the same-source comparisons to negligible for the different-source comparisons. Combining the outcomes of computer-based methods with the judgments of examiners could increase the validity of firearm examinations. The examiners' numerical degree-of-support judgments for their source decisions were not well-calibrated and showed clear signs of overconfidence. We suggest studying the merits of performance feedback to calibrate these judgments.

Bullet signature measurement with chromatic confocal sensor

A new, non-contact, three-dimensional (3D) bullet signature measuring system based on a chromatic confocal sensor is developed. The system is composed of a precision rotary table and a chromatic confocal sensor. The measurement uncertainty of the system is less than 1 µm. When measuring the surface topography of the object, the sensor acquires wavelength information reflected from the object instead of intensity information. This advantage is very suitable to bullet signature measurements. The chromatic confocal sensor works in the point measuring mode and can acquire data continuously with high speed. One round section measurement on the bullet body takes less than 1 minute.

Sharp force trauma with two katana swords: identifying the murder weapon by comparing tool marks on the skull bone

This paper describes the variety of information that a tool mark analysis on human tissue can provide based on a case of multiple sharp violence. The perpetrator attacked the victim with a sharp-edged weapon against the head, leaving several deep wounds on the back of the skull bone. Three of those marks on the skull bone could be used for a forensic tool mark examination. Silicone casts of the marks were compared by light microscopy with casts of test marks of Japanese katana swords found at the crime scene. One of the swords could be identified as the one responsible for the marks. In addition, the marks and the test marks were scanned in 3D and examined in a visual on-screen comparison confirming the results from the light microscopic examination. Furthermore, a mathematical approach in which the signatures of the marks from the skull bone and the test marks from the sword were compared by cross correlation confirms those findings. In addition, the aforementioned results were used to determine the orientation of the sword in relation to the cranial bone at the time of the respective impact.

A Bayesian approach based on Kalman filter frameworks for bullet identification

When a bullet is fired from a barrel, random imperfections in the interior surface of the barrel imprint 3-D micro structures on the bullet surface that are seen as striations. Despite being random and non-stationary in nature, these striations are known to be consistently reproduced in a unique pattern on every bullet. This is a key idea in bullet identification. Common procedures in the field of automatic bullet identification include extraction of a feature profile from bullet image, profile smoothing and comparison of profiles using normalized cross correlation. Since the cross correlation based comparison is susceptible to high-frequency noise and nonlinear baseline drift, profile smoothing is a critical step in bullet identification. In previous work, we considered bullet images as nonlinear non-stationary processes and applied ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. Using EEMD, each bullet average profile was decomposed into several scales known as intrinsic mode functions (IMFs). By choosing an appropriate range of scales, the resultant smoothed profile contained less high-frequency noise and no nonlinear baseline drift. But the procedure of choosing the proper number of IMFs to reduce the high-frequency noise effect was manual. This poses a problem in comparison of bullets whose images contained less or more noise in comparison to others because their useful information may be present in the corresponding discarded IMFs. Moreover, another problem arises when the bullet type changes. In this case manual inspection is needed once more to figure out which range of IMFs contain less high-frequency noise for this particular type of bullet. In this paper, we propose a novel combination of EEMD and Bayesian Kalman filter to solve these problems. First the bullet images are rotated using Radon transform. The rotated images are averaged column-wise to acquire averaged 1-D profiles. The nonlinear baseline drifts of averaged profiles are removed using EEMD algorithm. The profiles are then processed by a Kalman filter that is designed to automatically and optimally reduce the effect of high-frequency noise. Using Expectation Maximization (EM) technique, for each averaged profile, the parameters of Kalman filter are reconfigured to optimally suppress the high-frequency noise in each averaged profile. This work is the first effort that practically implements the Kalman filter for optimal denoising of firearm image profiles. In addition, we believe that Euclidean distance metric can help the normalized cross-correlation based comparison. Therefore, in this paper, we propose a comparison metric that is invariant to start and endpoints of firearm image profiles. This metric combines the prized properties of both Euclidean and normalized cross-correlation metrics in order to improve identification results. The proposed algorithm was evaluated on a database containing 180 2-D gray-scale images acquired from bullets fired from different AK-47 assault rifles. Although the proposed method needs more calculations in comparison to conventional methods, the experiments showed that it attained better results compared with the conventional methods and the previous method based on EMD in the field of automatic bullet identification.

A Multimodal Fusion Approach for Bullet Identification Systems

In the field of forensic science, bullet identification is based on the fact that firing the cartridge from a barrel leaves exclusive microscopic striation on the fired bullets as the fingerprint of the firearm. The bullet identification methods are categorized in 2-D and 3-D based on their image acquisition techniques. In this study, we focus on 2-D optical images using a multimodal technique and propose several distinct methods as its modalities. The proposed method uses a multimodal rule-based linear weighted fusion approach which combines the semantic level decisions from different modalities with a linear technique that its optimized modalities weights have been identified by the genetic algorithm. The proposed approach was applied on a dataset, which includes 180 2-D bullet images fired from 90 different AK-47 barrels. The experimentations showed that our approach attained better results compared to common methods in the field of bullet identification.

Adapting the Chumbley Score to Match Striae on Land Engraved Areas (LEAs) of Bullets

The same-source problem remains a major challenge in forensic toolmark and firearm examination. Here, we investigate the applicability of the Chumbley method (J Forensic Sci, 2018, 63, 849; J Forensic Sci, 2010, 55, 953) (10,12), developed for screwdriver markings, for same-source identification of striations on bullet LEAs. The Hamby datasets 44 and 252 measured by NIST and CSAFE (high-resolution scans) are used here. We provide methods to identify parameters that minimize error rates for matching of LEAs, and a remedial algorithm to alleviate the problem of failed tests, while increasing the power of the test and reducing error rates. For 85,491 land-to-land comparisons (84,235 known nonmatches and 1256 known matches), the adapted test does not provide a result in 176 situations (originally more than 500). The Type I and Type II error rates are 7.2% (6105 out of 84,235) and 21.4% (271 out of 1256), respectively. This puts the proposed method on similar footing as other single-feature matching approaches in the literature.

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.

Applying 3D measurements and computer matching algorithms to two firearm examination proficiency tests

In order for a crime laboratory to assess a firearms examiner's training, skills, experience, and aptitude, it is necessary for the examiner to participate in proficiency testing. As computer algorithms for comparisons of pattern evidence become more prevalent, it is of interest to test algorithm performance as well, using these same proficiency examinations. This article demonstrates the use of the Congruent Matching Cell (CMC) algorithm to compare 3D topography measurements of breech face impressions and firing pin impressions from a previously distributed firearms proficiency test. In addition, the algorithm is used to analyze the distribution of many comparisons from a collection of cartridge cases used to construct another recent set of proficiency tests. These results are provided along with visualizations that help to relate the features used in optical comparisons by examiners to the features used by computer comparison algorithms.

Standard Reference Specimens in Quality Control of Engineering Surfaces

In the quality control of engineering surfaces, we aim to understand and maintain a good relationship between the manufacturing process and surface function. This is achieved by controlling the surface texture. The control process involves: 1) learning the functional parameters and their control values through controlled experiments or through a long history of production and use; 2) maintaining high accuracy and reproducibility with measurements not only of roughness calibration specimens but also of real engineering parts. In this paper, the characteristics, utilizations, and limitations of different classes of precision roughness calibration specimens are described. A measuring procedure of engineering surfaces, based on the calibration procedure of roughness specimens at NIST, is proposed. This procedure involves utilization of check specimens with waveform, wavelength, and other roughness parameters similar to functioning engineering surfaces. These check specimens would be certified under standardized reference measuring conditions, or by a reference instrument, and could be used for overall checking of the measuring procedure and for maintaining accuracy and agreement in engineering surface measurement. The concept of "surface texture design" is also suggested, which involves designing the engineering surface texture, the manufacturing process, and the quality control procedure to meet the optimal functional needs.