A Set of Handwriting Features for Use in Automated Writer Identification1, 2. Issue 3 (5th January 2017)

• Record Type: Journal Article
• Authors: Miller, John J.; Patterson, Robert Bradley; Gantz, Donald T.; Saunders, Christopher P.; Walch, Mark A.; Buscaglia, JoAnn
• Is Part Of: Journal of forensic sciences. Volume 62:Issue 3(2017)
• Page Start: 722
• Page End: 734
• Publication Date: 2017-01-05
• Subjects: forensic science -- document examination -- handwriting -- writer identification -- writing biometric -- automated biometric; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.13345
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph‐based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph‐based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph‐based system described in this article has been implemented in a highly accurate and approximately language‐independent biometric recognition system of writers of cursive documents.