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

Bachrach, Benjamin; Jain, Anurag; Jung, Sung Hoon; Koons, Robert D.

doi:10.1111/j.1556-4029.2009.01221.x

Cited by 44 publications

(68 citation statements)

References 7 publications

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“…In particular, while it is generally understood that the "length characteristics" of class and individual characteristics are relatively longer and shorter, respectively as was suggested by Bachrach et al (2010), it is hard to defend the premise that selecting a choice of smoother span by simple graphical examination (as we have done here) reasonably corresponds to separation of class and individual marks. And even if so, the claim that there is enough information in one set of residuals to build a statistical model representing the population of class characteristics is clearly also debatable.…”

Section: Discussionmentioning

confidence: 89%

“…Quantifying the entire tool mark opens the door to more objective methods of mark comparison which result in a single numerical index of similarity between two marks. Bachrach et al (2010) proposed using the cross correlation function, which he calls the relative distance between two marks, to quantify the degree of match. Chumbley et al (2010) suggested using a Mann-Whitney U-statistic, which is described in the next subsection, to quantify the match.…”

Section: Tool Mark Comparison Backgroundmentioning

confidence: 99%

“…For a more thorough background, see Hoeksema and Morris (2013). Despite the numerous proposals, "There are no standard methods for the application of probability and statistics to the analysis of tool mark evidence" (Petraco et al 2012, p. 901) Several numerical approaches rely on a single index of similarity to compare two tool marks; two examples are described by Bachrach et al (2010) and Chumbley et al (2010). Hoeksema and Morris (2013) proposed a multivariate analysis using as data any normally distributed similarity index in which two samples of comparisons, one from field tool mark with lab tool mark comparisons (field-lab) and one from lab mark with lab mark comparisons (lab-lab), are compared using a likelihood ratio test (LRT).…”

Section: Introductionmentioning

confidence: 99%

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Statistical methods for the forensic analysis of striated tool marks

Hoeksema

2013

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Section: Discussionmentioning

confidence: 89%

Section: Tool Mark Comparison Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical methods for the forensic analysis of striated tool marks

Hoeksema

2013

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“…Methods for matching striated marks for a variety of tools have been studied in the literature (see Table for an overview): In , digitized screwdriver marks have been analyzed using a profilometer; in 3D marks from screwdriver, tongue and groove pliers captured using a confocal microscope have been investigated; digitized marks from slip‐joint pliers generated by a surface profilometer have been investigated in .…”

Section: Error Rates In Same‐source Analyses For Striated Toolmarks Rmentioning

confidence: 99%

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

Krishnan

Hofmann

2018

Journal of Forensic Sciences

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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.

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“…00 % and 49 . 5 % [16]. The fractal nature of biologic shapes can be exploited for injured regions and their bounding transition areas to surrounding unharmed skin: they contain relevant geometry on multiple scale levels.…”

Section: Introductionmentioning

confidence: 99%

Skin injury model classification based on shape vector analysis

2012

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Background: Skin injuries can be crucial in judicial decision making. Forensic experts base their classification on subjective opinions. This study investigates whether known classes of simulated skin injuries are correctly classified statistically based on 3D surface models and derived numerical shape descriptors.Methods: Skin injury surface characteristics are simulated with plasticine. Six injury classes – abrasions, incised wounds, gunshot entry wounds, smooth and textured strangulation marks as well as patterned injuries - with 18 instances each are used for a k-fold cross validation with six partitions. Deformed plasticine models are captured with a 3D surface scanner. Mean curvature is estimated for each polygon surface vertex. Subsequently, distance distributions and derived aspect ratios, convex hulls, concentric spheres, hyperbolic points and Fourier transforms are used to generate 1284-dimensional shape vectors. Subsequent descriptor reduction maximizing SNR (signal-to-noise ratio) result in an average of 41 descriptors (varying across k-folds). With non-normal multivariate distribution of heteroskedastic data, requirements for LDA (linear discriminant analysis) are not met. Thus, shrinkage parameters of RDA (regularized discriminant analysis) are optimized yielding a best performance with λ = 0.99 and γ = 0.001.Results: Receiver Operating Characteristic of a descriptive RDA yields an ideal Area Under the Curve of 1.0for all six categories. Predictive RDA results in an average CRR (correct recognition rate) of 97,22% under a 6 partition k-fold. Adding uniform noise within the range of one standard deviation degrades the average CRR to 71,3%.Conclusions: Digitized 3D surface shape data can be used to automatically classify idealized shape models of simulated skin injuries. Deriving some well established descriptors such as histograms, saddle shape of hyperbolic points or convex hulls with subsequent reduction of dimensionality while maximizing SNR seem to work well for the data at hand, as predictive RDA results in CRR of 97,22%. Objective basis for discrimination of non-overlapping hypotheses or categories are a major issue in medicolegal skin injury analysis and that is where this method appears to be strong. Technical surface quality is important in that adding noise clearly degrades CRR.Trial registration: This study does not cover the results of a controlled health care intervention as only plasticine was used. Thus, there was no trial registration.

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

Cited by 44 publications

References 7 publications

Statistical methods for the forensic analysis of striated tool marks

Statistical methods for the forensic analysis of striated tool marks

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

Skin injury model classification based on shape vector analysis

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