Quantifying Bayes Factors for Forensic Handwriting Evidence
This presentation is from the 76th Annual Conference of the American Academy of Forensic Sciences (AAFS), Denver, Colorado, February 19-24, 2024.
Quantifying Writer Variance Through Rainbow Triangle Graph Decomposition
This presentation is from the 76th Annual Conference of the American Academy of Forensic Sciences (AAFS), Denver, Colorado, February 19-24, 2024
A deep learning approach for the comparison of handwritten documents using latent feature vectors
Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare
A statistical approach to aid examiners in the forensic analysis of handwriting
We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal is to compute a posterior probability of writership of a
CSAFE Project Update & ASCLD FRC Collaboration
This presentation highlighted CSAFE’s collaboration with the ASCLD FRC Collaboration Hub.
Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data
Identifying the source of handwriting is an important application in the field of forensic science that addresses questioned document evidence found in criminal cases and civil litigation. It is difficult,
Quantifying Bayes Factors for Forensic Handwriting Evidence
Questioned Document Examiners (QDEs) are tasked with analyzing handwriting evidence to make source (or writership) determinations. The Center for Statistics and Applications of Forensic Evidence (CSAFE) has previously developed computational
Quantifying Writer Variance Through Rainbow Triangle Graph Decomposition of the Common Word “the”
Handwriting comparative analysis is based on the principle that no two individuals can produce the same writing and that an individual cannot exactly reproduce his/her handwriting. This project aims to
An Overview of the Two-Stage, Score-Based Likelihood Ratio, and Bayes Factor Approaches for Writership Determinations
A variety of statistical approaches have been developed at the Center for Statistics and Applications in Forensic Evidence (CSAFE) to address the question of writership for forensic document examinations. Previous
A Rotation-Based Feature and Bayesian Hierarchical Model for the Forensic Evaluation of Handwriting Evidence in a Closed Set
Forensic handwriting examiners are often tasked with identifying the writer of a particular document. Examples of handwriting evidence include ransom notes, forged documents and signatures, and threatening letters. At present,