Miller John J, Patterson Robert Bradley, Gantz Donald T, Saunders Christopher P, Walch Mark A, Buscaglia JoAnn
George Mason University, Document Forensics Laboratory, Volgenau School of Engineering, Nguyen Engineering Building, 4400 University Drive, Fairfax, 22030, VA.
Department of Mathematics and Statistics, South Dakota State University, AME Building, Box 2225, Brookings, SD, 57006, USA.
J Forensic Sci. 2017 May;62(3):722-734. doi: 10.1111/1556-4029.13345. Epub 2017 Jan 5.
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.
作者的生物特征身份可以通过物理特征测量的分布（“作者轮廓”）来表征；本文描述了一种基于图形的系统，该系统有助于对这些特征进行量化。为了实现这种量化，手写内容被分割成基本的图形形式（“字素”），然后对其进行“骨架化”以生成手写片段的图形拓扑结构。基于图形的匹配算法首先根据字素的图形拓扑结构，然后根据其几何特征对字素进行比较。从已知作者处获得的图形可以与从未知手写内容中提取的图形进行比较。该过程计算量很大，并且严重依赖于统计模式识别算法。本文重点关注这些物理特征的量化以及构建相关的模式识别方法，以便利用这些特征来区分不同的作者。本文所述的基于图形的系统已在一个高度准确且几乎与语言无关的手写体文档作者生物特征识别系统中得以实现。
