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,
Comparing handwriter and FLASH ID®, Two Handwriting Analysis Programs
FLASH ID and handwriter are computer programs that compare questioned handwritten documents against handwritten samples from known writers. FLASH ID was developed by Sciometrics and is used by the FBI,
Error Rate Methods for Forensic Handwriting Identification
Presentation is from the 106th International Association for Identification (IAI) Annual Educational Conference
Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data
Primary goals are to examine: 1. Write diversification versus representation. 2. Preservation of handwriting structure versus image density. 3. Input size versus training size. 4. Writer identification complexity assessment using
Center for Statistics and Application in Forensic Evidence Update
The information below highlights a sample of current research initiatives led by the CSAFE team. Additional accomplishments in other forensic science disciplines will be discussed in subsequent issues of Forensic