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Author

A Headrick

Bio: A Headrick is an academic researcher. The author has contributed to research in topics: Handwriting & Questioned document examination. The author has an hindex of 2, co-authored 8 publications receiving 271 citations.

Papers
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Book
15 Apr 1999
TL;DR: A Handwriting Compendium for Document Examiners discusses the Discrimination of Handwriting, the Premises for the Identification of handwriting, and the Fundamentals of the Identification Process.
Abstract: Introduction: The World of Documents The History of Questioned Document Examination -In Brief A Handwriting Compendium for Document Examiners The Discrimination of Handwriting The Premises for the Identification of Handwriting The Fundamentals of the Identification Process The Discrimination and Identification of Writing Special Problems in the Discrimination and Identification of Writing The Extrinsical Factors Influencing Handwriting The Intrinsical Influences upon Handwriting The Requirements and the Results The Diagnosis of Writing Identification The Scope of Document Examination The Sources of Document Examiners Science, Scientific Method and Writing Identification Graphology Understanding the Terms-Glossary Epilogue

281 citations

Journal ArticleDOI
TL;DR: A more definitive answer to the age-old question: ‘Is handwriting identification a science?’ is provided and some corrective suggestions are provided.

4 citations


Cited by
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Book
06 Nov 2003
TL;DR: This complete, technical guide details the principles, methods, technologies, and core ideas used in biometric authentication systems and defines and explains how to measure the performance of both verification and identification systems.
Abstract: This complete, technical guide details the principles, methods, technologies, and core ideas used in biometric authentication systems. It explains the definition and measurement of performance and examines the factors involved in choosing between different biometrics. It also delves into practical applications and covers a number of topics critical for successful system integration. These include recognition accuracy, total cost of ownership, acquisition and processing speed, intrinsic and system security, privacy and legal requirements, and user acceptance. The "Guide to Biometrics:" * Debunks myths and candidly confronts problems associated with biometrics research * Details relevant issues in choosing between biometrics, as well as defining and measuring performance * Defines and explains how to measure the performance of both verification and identification systems * Addresses challenges in managing tradeoffs between security and convenience Security and financial administrators, computer science professionals, and biometric systems developers will all benefit from an enhanced understanding of this important technology.

658 citations

Journal ArticleDOI
TL;DR: In this article, the authors used computer algorithms for extracting features from scanned images of handwriting, e.g., line separation, slant, character shapes, etc., to quantitatively establish individuality by using machine learning approaches.
Abstract: Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.

506 citations

Journal ArticleDOI
TL;DR: New and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality are developed.
Abstract: The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates

468 citations

Journal ArticleDOI
TL;DR: An effective method for automatic writer recognition from unconstrained handwritten text images based on the presence of redundant patterns in the writing and its visual attributes is proposed, which exhibits promising results on writer identification and verification.

204 citations

Journal ArticleDOI
TL;DR: A systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario is reported, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
Abstract: Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.

184 citations