title: Forensic surface metrology: tool mark evidence.
abstract: 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.
authors.affiliation: Department of Sciences, John Jay College of Criminal Justice, City University of New York, New York, USA.
authors.forename: Carol
authors.initials: C
authors.lastname: Gambino
authors.forename: Patrick
authors.initials: P
authors.lastname: McLaughlin
authors.forename: Loretta
authors.initials: L
authors.lastname: Kuo
authors.forename: Frani
authors.initials: F
authors.lastname: Kammerman
authors.forename: Peter
authors.initials: P
authors.lastname: Shenkin
authors.forename: Peter
authors.initials: P
authors.lastname: Diaczuk
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petraco
authors.forename: James
authors.initials: J
authors.lastname: Hamby
authors.forename: Nicholas D K
authors.initials: ND
authors.lastname: Petraco
mesh_terms: D000465:Algorithms; D005389:Firearms / Q000592:standards*; D005554:Forensic Medicine / Q000379:methods* / Q000592:standards*; D021621:Imaging, Three-Dimensional; D018613:Microscopy, Confocal; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D013223:Statistics as Topic; D060388:Support Vector Machine; D013499:Surface Properties