Document Type

Dissertation - Open Access

Award Date

2023

Degree Name

Doctor of Philosophy (PhD)

Department / School

Mathematics and Statistics

First Advisor

Christopher Saunders

Keywords

artificial intelligence automated, identification systems, black-box, generalized least squares, handwriting, white-box

Abstract

Handwriting analysis is a complex field largely living in forensic science and the legal realm. One task of a forensic document examiner (FDE) may be to determine the writer(s) of handwritten documents. Automated identification systems (AIS) were built to aid FDEs in their examinations. Part of the uses of these AIS (such as FISH[5] [7],WANDA [6], CEDAR-FOX [17], and FLASHID®2) are tomeasure features about a handwriting sample and to provide the user with a numeric value of the evidence. These systems use their own algorithms and definitions of features to quantify the writing and can be considered a black-box. The outputs of two AIS are used to compare to the results of a survey of FDE writership opinions. In this dissertation I will be focusing on the development of a response surface that characterizes the feature outputs of AIS outputs. Using a set of handwriting samples, a pairwise metric, or scoring method, is applied to each of the individual features provided by the AIS to produce sets of pairwise scores. The pairwise scores lead to a degenerate U-statistic. We use a generalized least squares method to test the null hypothesis that there is no relationship between two metrics (β1 = 0.) Monte Carlo simulations are developed and ran to ensure the results, considering the structure of the pairwisemetric, behave under the null hypothesis, and to ensure the modeling will catch a relationship under the alternative hypothesis. The outcome of the significance tests helps to determine which of the metrics are related to each other.

Publisher

South Dakota State University

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Rights Statement

In Copyright