Striated Tool Marks Research Articles

An Open-Source Implementation of the CMPS Algorithm for Assessing Similarity of Bullets

In this paper, we introduce the R package cmpsR, an open-source implementation of the Congruent Matching Profile Segments (CMPS) method developed at the National Institute of Standards and Technology (NIST) for objective comparison of striated tool marks. The functionality of the package is showcased by examples of bullet signatures that come with the package. Graphing tools are implemented in the package as well for users to assess and understand the CMPS results. Initial tests were performed on bullet signatures generated from two sets of 3D scans in the Hamby study under the framework suggested by the R package `bulletxtrctr`. New metrics based on CMPS scores are introduced and compared with existing metrics. A measure called sum of squares ratio is included, and how it can be used for evaluating different scans, metrics, or parameters is showcased with the Hamby study data sets. An open-source implementation of the CMPS algorithm makes the algorithm more accessible, generates reproducible results, and facilitates further studies of the algorithm such as method comparisons.

Fired bullet signature correlation using the Congruent Matching Profile Segments (CMPS) method

We introduce the Congruent Matching Profile Segments (CMPS) method for objective comparison of striated tool marks and apply it to bullet signature correlations. The method is derived from the congruent matching cell (CMC) method developed for the comparison of impressed tool marks. The proposed method is designed to increase comparison accuracy by addressing the comparison challenges caused by striae profiles with different lateral scales, varying vertical (height) scales, and sections that are poorly marked or have little to no similarity. Instead of correlating the entire profiles extracted from striated tool marks, the method divides one of the compared profiles into segments. Each segment is then correlated with the other profile. The CMPS method uses the normalized cross-correlation function with multiple correlation peak inspection to determine the number of profile segments that have both significant topography similarity and a congruent registration position. Initial tests were performed on the land engraved areas (LEAs) of 35 bullets fired from 10 consecutively manufactured pistol barrels. The results show clear separation between the CMPS scores of the 549 known non-matching (KNM) LEA profiles and the 46 known matching (KM) LEA profiles. These results are an improvement over those obtained using the correlation coefficient score of whole profiles. The large number of CMPS segment correlations may facilitate a statistical approach to error rate estimations.

Retrieval of striated toolmarks using convolutional neural networks

The authors propose TripNet as method for calculating similarities between striated toolmark images. The objective for this system is detecting and comparing characteristics of the tools while being invariant to varying parameters like angle of attack, substrate material, and lighting conditions. Instead of designing a handcrafted feature extractor customised for this task, the authors propose the use of a convolutional neural network. With the proposed system, one‐dimensional profiles extracted from images of striated toolmarks are mapped into an embedding. The system is trained by minimising a triplet loss function, so that a similarity measure is defined by the distance in this embedding. The performance is evaluated on the NFI Toolmark database containing 300 striated toolmarks of screwdrivers published by the Netherlands Forensic Institute. The system proposed is able to adapt to a large range of angles of attack, achieving a mean average precision of 0.95 for toolmark comparisons with differences in angle of attack of –. Furthermore, four different triplet selection approaches are proposed and their effect on the retrieval of toolmarks from a database of unseen tools is evaluated in detail.

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.

Virtual and simulated striated toolmarks for forensic applications

Large numbers of experimental toolmarks of screwdrivers are often required in casework of toolmark examiners and in research environments alike, to be able to recover the angle of attack of a crime scene mark and to determine statistically meaningful properties of toolmarks respectively. However, in practice the number of marks is limited by the time needed to create them.In this article, we present an approach to predict how a striated mark of a particular tool would look like, using 3D surface datasets of screwdrivers. We compare these virtual toolmarks qualitatively and quantitatively with real experimental marks in wax and show that they are very similar. In addition we study toolmark similarity, dependent on the angle of attack, with a very high angular resolution of 1°. The results show that for the tested type of screwdriver, our toolmark comparison framework yields known match similarity scores that are above the mean known non-match similarity scores, even for known match differences in angle of attack of up to 40°. In addition we demonstrate an approach to automatically recover the angle of attack of an experimental toolmark and experiments yield high accuracy and precision of 0.618±4.179°. Furthermore, we present a strategy to study the structural elements of striated toolmarks using wavelet analysis, and show how to use the results to simulate realistic toolmarks.

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.

Addressing the National Academy of Sciences’ Challenge: A Method for Statistical Pattern Comparison of Striated Tool Marks

In February 2009, the National Academy of Sciences published a report entitled "Strengthening Forensic Science in the United States: A Path Forward." The report notes research studies must be performed to "…understand the reliability and repeatability…" of comparison methods commonly used in forensic science. Numerical classification methods have the ability to assign objective quantitative measures to these words. In this study, reproducible sets of ideal striation patterns were made with nine slotted screwdrivers, encoded into high-dimensional feature vectors, and subjected to multiple statistical pattern recognition methods. The specific methods employed were chosen because of their long peer-reviewed track records, widespread successful use for both industry and academic applications, rely on few assumptions on the data's underlying distribution, can be accompanied by standard confidence levels, and are falsifiable. For PLS-DA, correct classification rates of 97% or higher were achieved by retaining only eight dimensions (8D) of data. PCA-SVM required even fewer dimensions, 4D, for the same level of performance. Finally, for the first time in forensic science, it is shown how to use conformal prediction theory to compute identifications of striation patterns at a given level of confidence.

Forensic surface metrology: tool mark evidence

Over the last several decades, forensic examiners of impression evidence have come under scrutiny in the courtroom due to analysis methods that rely heavily on subjective morphological comparisons. Currently, there is no universally accepted system that generates numerical data to independently corroborate visual comparisons. Our research attempts to develop such a system for tool mark evidence, proposing a methodology that objectively evaluates the association of striated tool marks with the tools that generated them. In our study, 58 primer shear marks on 9 mm cartridge cases, fired from four Glock model 19 pistols, were collected using high-resolution white light confocal microscopy. The resulting three-dimensional surface topographies were filtered to extract all "waviness surfaces"-the essential "line" information that firearm and tool mark examiners view under a microscope. Extracted waviness profiles were processed with principal component analysis (PCA) for dimension reduction. Support vector machines (SVM) were used to make the profile-gun associations, and conformal prediction theory (CPT) for establishing confidence levels. At the 95% confidence level, CPT coupled with PCA-SVM yielded an empirical error rate of 3.5%. Complementary, bootstrap-based computations for estimated error rates were 0%, indicating that the error rate for the algorithmic procedure is likely to remain low on larger data sets. Finally, suggestions are made for practical courtroom application of CPT for assigning levels of confidence to SVM identifications of tool marks recorded with confocal microscopy.

A Statistical Validation of the Individuality and Repeatability of Striated Tool Marks: Screwdrivers and Tongue and Groove Pliers*

Tool mark identification relies on the premise that microscopic imperfections on a tool's working surface are sufficiently unique and faithfully transferred to enable a one-to-one association between a tool and the tool marks it creates. This paper presents a study undertaken to assess the validity of this premise. As part of this study sets of striated tool marks were created under different conditions and on different media. The topography of these tool marks was acquired and the degree of similarity between them was quantified using well-defined metrics. An analysis of the resulting matching and nonmatching similarity distributions shows nearly error-free identification under most conditions. These results provide substantial support for the validity of the premise of tool mark identification. Because the approach taken in this study relies on a quantifiable similarity metric, the results have greater repeatability and objectivity than those obtained using less precise measures of similarity.

Estimation of Bullet Striation Similarity Using Neural Networks

A new method that searches for similar striation patterns using neural networks is described. Neural networks have been developed based on the human brain, which is good at pattern recognition. Therefore, neural networks would be expected to be effective in identifying striated toolmarks on bullets. The neural networks used in this study deal with binary signals derived from striation images. This signal plays a significant role in identification, because this signal is the key to the individually of the striations. The neural network searches a database for similar striations by means of these binary signals. The neural network used here is a multilayer network consisting of 96 neurons in the input layer, 15 neurons in the middle, and one neuron in the output layer. Two signals are inputted into the network and a score is estimated based on the similarity of these signals. For this purpose, the network is assigned to a previous learning. To initially test the validity of the procedure, the network identifies artificial patterns that are randomly produced on a personal computer. The results were acceptable and showed robustness for the deformation of patterns. Moreover, with ten unidentified bullets and ten database bullets, the network consistently was able to select the correct pair.