Date of Award

Spring 6-2018

Document Type

Thesis

Degree Name

Master of Science (MS)

Department/Program

Forensic Science

Language

English

First Advisor

Nicholas D.K. Petraco

Second Reader

Peter Shenkin

Abstract

Subclass characteristics on bullets may mislead firearm examiners when they rely on traditional 2D images. In order to provide indelible examples for training and help avoid identification errors, 3D topography surface maps and statistical methods of pattern recognition are applied to toolmarks on bullets containing known subclass characteristics. This research was conducted by collecting 3D topography surface map data from land engraved areas of bullets fired through known barrels. This data was processed and used to train the statistical algorithms to predict their origin. The results from the algorithm are compared with the “right answers” (i.e. correct IDs) of the bullets in order to examine how well computational methods can work if subclass is present.

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