Bullet Identification Research Articles

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.

Glock Marking Barrels – An Evaluation of Individual and Subclass Characteristics

Glock Inc. recently replaced the Enhanced Bullet Identification System (EBIS) with what Glock refers to as the Glock Marking Barrel. Casts and test fired bullets from thirty-five Glock Marking Barrels were evaluated for subclass and individual characteristics. It was determined that the barrels had subclass carryover from barrel to barrel manufactured using the same mandrel. However, identification of a fired bullet back to the barrel/firearm that fired it was still possible using specific areas of the fired bullet. Due to the fact that this type of barrel will be used in all models of Glock pistols, an examiner should be aware of which areas of a fired bullet may have a high potential for subclass characteristics.

Automated Firearm Classification From Bullet Markings Using Deep Learning

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.

Pilot study on deformed bullet correlation

Most studies on bullet identification address test fired bullets that have near pristine striated marks on the land engraved areas (LEAs). However, in case work, bullets found at a crime scene may be severely deformed or fragmented. The resulting missing, expanded, or distorted LEA striations can cause challenges in toolmark comparisons performed by examiners or algorithms. In this paper, an image reconstruction procedure is proposed that, in combination with the Congruent Matching Profile Segments (CMPS) profile comparison method, facilitates the algorithmic correlation of deformed bullets. Initial validation tests were conducted using 57 bullets, with varying degrees of fragmentation or deformation, that were fired from the same 9mm Luger caliber Luger pistol. The bullets spanned 7 different ammunition brands. The CMPS method was applied to correlate the LEA striation profiles extracted from LEA topography images that were corrected for pattern distortion. 15 bullet LEAs, out of 250 bullet LEAs that could be measured, had major distortions. Two sets of comparison tests were conducted, corresponding to a same source and specific source scenario: 1) comparison of the severely distorted LEAs with a near-pristine reference bullet, before and after image reconstruction, and 2) inter comparisons of distorted LEAs, before and after reconstruction. The reconstruction process significantly improved the correlation results when dealing with distorted bullet LEAs. In general, the improvement was larger for samples with relatively large deformation and good striation visibility. Samples with approximately parallel striations tend to have less improvement of CMPS results after profile reconstruction since the CMPS method itself can correct certain scale errors.

Objective Identification of Bullets Based on 3D Pattern Matching and Line Counting Scores

In firearm identification, a firearm examiner looks at a pair of fired bullets or cartridge cases using a comparison microscope and determines from this visual analysis if they were both fired from the same firearm. In the particular case of fired bullets, the individual firearm signature takes the form of a striated pattern. Over the time, the firearm examiner’s community developed two distinct approaches for bullet identification: pattern matching and line counting. More recently, the emergence of technology enabling the capture of surface topographies down to a submicron depth resolution has been a catalyst for the field of computerized objective ballistic identification. Objectiveness is achieved through the statistical analysis of various scores of known matches and known nonmatches exhibit pair comparison, which in turn implies the capture of large quantities of bullets and cartridge cases topographies. The main goal of this study was to develop an objective identification method for bullets fired from conventionally rifled barrels, and to test this method on public and proprietary bullet 3D image datasets captured at different lateral resolutions. Two newly developed bullet identification scores, the Line Counting Score (LCS) and the Pattern Matching Score, computed on 3D topographies yielded perfect match versus nonmatch separation for three different sets used in the standard Hamby–Brundage Test. A similar analysis performed using a larger, more-realistic set, enabled us to define a discriminative line at a false match rate of 1/10[Formula: see text]000 on a 2D plot that shows both identification scores for matches and nonmatches. The LCS is shown to produce a better sensitivity than the standard consecutive matching striae criteria for the more-realistic dataset. A likelihood function was also computed from a linear combination of both scores, and a conservative approach based on extreme value theory is proposed to extrapolate this function in the score domain where nonmatch data are not available. This study also provides a better understanding of the limitations of studies that involve very few firearms.

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.

A correlation based bullet identification method using empirical mode decomposition

The striations on bullet surface are 3D micro structures formed when a bullet is forcing its way out of barrel. Each barrel leaves individual striation patterns on bullets. Hence, the striation information of bullets is helpful for firearm identification. Common automatic identification methods process these images using linear time invariant (LTI) filters based on correlation. These methods do not consider the sensitivity of correlation based comparisons to nonlinear baseline drifts. The striations are undeniably random unique micro structures caused by random non-model-based imperfections in the tools used in rifling process, therefore any characteristic profile that is extracted from a bullet image is statistically non-stationary. Due to limitations of LTI filters, using them in smoothing bullet images and profiles may cause information loss and impact the process of identification. To address these problems, in this article, we consider bullet images as nonlinear non-stationary processes and propose a novel method which uses ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. The features extracted by EEMD algorithm not only contain less noise, but also have no nonlinear baseline drifts. These improvements help the correlation based comparison methods to perform more robustly and efficiently. The experiments showed that our proposed method attained better results compared with two common methods in the field of automatic bullet identification.

Histogram Intersectionを用いた弾丸固有の線条痕の絞込み手法の提案

Bullet identification requires special skills and takes a long time. So novel method is expected to be developed for assisting this skillful and time-consuming work. The discrimination of two bullets needs similarity quantification of striated toolmarks most. But conventional method based on image correlation is not applicable because of lower striated toolmark similarity. In this paper, a new method based on intersection which extracts common elements is proposed to measure similarity correctly between striated toolmarks of bullets and makes it possible to extract inherent striated toolmarks of used gun. The effectiveness of the proposed method is confirmed through experiments.

弾丸異同識別のための線条痕の重み付けと位置を保存したHistogram Intersectionによる類似性評価手法の提案

Bullet identification requires special skills and takes a long time. So novel method is expected to be developed for assisting this skillful and time-consuming work. The discrimination of two bullets needs similarity quantification of striated toolmarks most. But conventional method based on image correlation is not applicable because of lower striated toolmark similarity. In this paper, a new method is proposed to measure similarity correctly between striated toolmarks of bullets and makes it possible to extract inherent striated toolmarks of used gun. The effectiveness of the proposed method is confirmed through experiments.

Automated bullet identification based on striation feature using 3D laser color scanner

A procedure for automated bullet identification is described based on striation measurement using the bullet 3D digital representations obtained by a laser color scanner. When we do bullet identification by eye, we try to best match the striations according to the order of the striations and the distance between them. Based on this principle, individual characteristics which describe the unique striation pattern on the bullet are extracted. Experimental results prove that our algorithm can find the bullets with high trace similarity correctly and do bullet identification successfully.

画像解析に基づく線条痕の特徴強調と局所照合による発射弾丸の異同識別

Bullet identification is the difficult work that only specialists can do. It requires a high level of skill and takes a long time. So we are trying to assist appraising workers by using an image processing technology. In this paper, we propose a method to express the similarity between two bullets numerically and to estimate whether two bullets were fired by the same firefream.

The performance of BALİSTİKA 2010 system for 9 mm × 19 mm and 7.65 mm × 17 mm cartridge case correlation

The firearm identification has two examination phases; the first phase is “one by one” cartridge case or bullet identification. The second phase is “Open Case File (OCF)” examination. Due to the size of the OCF archive, the OCF examination with only comparison microscopes takes a long time and is an unfeasible process. The Computerized Ballistic Identification Systems (CBIS) has become an essential tool for archive examination by correlation and preliminary eliminations. In this study, two objectives were pursued; the first is measuring the performance of the BALİSTİKA 2010 system on cartridge case acquisition of handguns, correlation and examination. The second objective is the examination of the performance on the correlation according to brand and models of firearms. Detailed experimental results are demonstrated for about 2000 cartridge cases.

線条痕顕在化と画像分割照合による発射弾丸の異同識別方式の検討

線条痕顕在化と画像分割照合による発射弾丸の異同識別方式の検討

Selecting Valid Correlation Areas for Automated Bullet Identification System Based on Striation Detection.

Some automated bullet identification systems calculate a correlation score between two land impressions to measure their similarity. When extracting a compressed profile from the land impression of a fired bullet, inclusion of areas that do not contain valid individual striation information may lead to sub-optimal extraction and therefore may deteriorate the correlation result. In this paper, an edge detection algorithm and selection process are used together to locate the edge points of all tool-mark features and filter out those not corresponding to striation marks. Edge points of the resulting striation marks are reserved and expanded to generate a mask image. By imposing the mask image on the topography image, the weakly striated area(s) are removed from the expressed profile extraction. Using this method, 48 bullets fired from 12 gun barrels of six manufacturers resulted in a higher matching rate than previous studies.

Striation Density for Predicting the Identifiability of Fired Bullets with Automated Inspection Systems*

Automated firearms identification systems will correlate a reference bullet with all evidence bullets without a selection procedure to exclude the bullets having insufficient bullet identifying signature. Correlations that include such bullets increase the workload and may affect the correlation accuracy. In this article, a parameter called striation density is proposed for determining and predicting bullet identifiability. After image preprocessing, edge detection and filtering techniques are used to extract the edges of striation marks, the resulting binary image distinctly shows the amount and distribution of striation marks. Then striation density is calculated for determining the quality of images. In the experiment, striation densities for six lands of 48 bullets fired from 12 gun barrels of six manufactures are calculated. Statistical results show strong relation between striation density and identification rate. It can provide firearms identification systems with a quantitative criterion to assess whether there are sufficient striae for reliable bullet identification.

Algorithm for Automatic 3-D Scanned Bullet Identification Based on the Principle of Human Vision

Bullet identification is a complicated work which demands high accuracy rate. In this paper, we try to find a reliable and fast automatic bullet identification algorithm using image processing technology. The key and most difficult step is to find an image feature which can describe the unique striation pattern on the bullet. When we do bullet identification by human-eye, we try to best match the striations of the bullets. It’s mainly according to the order of the striations and the distance between them. Based on this principle, a new algorithm for automatic bullet identification is proposed. Using the bullet’s visual graphic set which is offered by the Three-Dimensional Laser Color Scanner (3DLCS), we firstly do pre-processing to the bullet unwrapped image, such as image enhancement, edge detecting, binarization, thinning and denoising, to obtain an image with clear striations and low noises. Then we find an effective image feature based on the principle of human vision: the bullet feature vectors (BFVs). According to actual needs, we give a reasonable definition for vector distance (VD) between vectors of different dimensions, and take VD of BFVs as a measurement for bullet similarity. Experimental results prove that our algorithm can correctly find the bullets with high trace similarity and do bullet identification successfully.

Automated bullet-identification system based on surface topography techniques

Every firearm has individual characteristics that are as unique to it as fingerprints are to human beings. When a firearm is fired, it transfers these characteristics – in the form of microscopic scratches and dents – to the fired bullets and cartridge casings. The rifling of the barrel of the firearm marks the bullets travelling through it, and the firearm's breech mechanism marks the ammunition's cartridge casing. Characterising these marks is the critical element in identifying firearms. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. In the past decade, engineers have created automated ballistics identification systems that meld traditional comparison microscopes with digital cameras, computers, huge databases, and image analysis techniques. This kind of system can help investigators to link crimes by automatically finding similarities among images of bullet but suffering significant drawbacks and minimal matching. More recently, approaches based on 3D digital representations of evidence surface topography have started to appear, both in research and industrial products. Potentially the introduction of 3D surface topography measurement can overcome the limitations of digital imaging systems by making the bullet surface measurement reproducible and reliable. A 3D quantitative approach for bullet identification is proposed in this paper. In this system the surface topography of the whole bullet can be acquired for analysis and identification. Primary researches have been done by applying advanced surface topography techniques for bullet marks’ characteristics extraction. A variety of 2D and 3D visualization graphics have also been provided to help firearm examiners to make final decisions.

Bullet Identification: A Case of a Fatal Hunting Accident Resolved by Comparison of Lead Shot Using Instrumental Neutron Activation Analysis

Bullet identification by chemical analysis often provides a powerful clue in forensic science. A case is reported in which a hunting accident was resolved by using instrumental neutron activation analysis (INAA) for direct comparison of the trace element content in lead shot. Different preparation batches of lead shot appear to have a high within-group composition homogeneity, and good differentiation is achieved between different batches. Determination of the nickel and antimony content on a bush branch demonstrated that the branch had been perforated by one of the shot pellets, and this helped the detectives in reconstruction of the crime scene.

Bullet identification with radiography.

The authors provide a simple radiographic method for estimating bullet weight and caliber of both deformed and undeformed bullets that enables accurate determination of caliber for the gamut of bullet shapes, with known degrees of confidence. The weight-determination procedure is based on the correlation between bullet cross-sectional area, as derived from three orthogonal radiographs, and bullet weight, as determined from a data base of the properties of 48 bullets removed from humans. Different equations were developed for bullets weighing 5.8 g or less, or more than 5.8 g. For relatively undeformed bullets an additional method calculated caliber directly from the diameter of the bullet body on radiographs. Both methods enabled correct prediction of the weight and caliber of the bullets; if one method could not be used, results of the other were reliable. Testimony based on these results has been accepted in a local police case and may meet requirements for testimony in U.S. court cases involving gunshots.