A Set of Handwriting Features for Use in Automated Writer Identification.

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.

A novel approach for latent print identification using accurate overlays to prioritize reference prints.

A novel approach to automated fingerprint matching and scoring that produces accurate locally and nonlinearly adjusted overlays of a latent print onto each reference print in a corpus is described. The technology, which addresses challenges inherent to latent prints, provides the latent print examiner with a prioritized ranking of candidate reference prints based on the overlays of the latent onto each candidate print. In addition to supporting current latent print comparison practices, this approach can make it possible to return a greater number of AFIS candidate prints because the ranked overlays provide a substantial starting point for latent-to-reference print comparison. To provide the image information required to create an accurate overlay of a latent print onto a reference print, "Ridge-Specific Markers" (RSMs), which correspond to short continuous segments of a ridge or furrow, are introduced. RSMs are reliably associated with any specific local section of a ridge or a furrow using the geometric information available from the image. Latent prints are commonly fragmentary, with reduced clarity and limited minutiae (i.e., ridge endings and bifurcations). Even in the absence of traditional minutiae, latent prints contain very important information in their ridges that permit automated matching using RSMs. No print orientation or information beyond the RSMs is required to generate the overlays. This automated process is applied to the 88 good quality latent prints in the NIST Special Database (SD) 27. Nonlinear overlays of each latent were produced onto all of the 88 reference prints in the NIST SD27. With fully automated processing, the true mate reference prints were ranked in the first candidate position for 80.7% of the latents tested, and 89.8% of the true mate reference prints ranked in the top ten positions. After manual post-processing of those latents for which the true mate reference print was not ranked first, these frequencies increased to 90.9% (1st rank) and 96.6% (top ten), respectively. Because the computational process is highly parallelizable, it is feasible for this method to work with a reference corpus of several thousand prints.

Innovative surveillance methods for rapid detection of disease outbreaks and bioterrorism: results of an interagency workshop on health indicator surveillance.

A system designed to rapidly identify an infectious disease outbreak or bioterrorism attack and provide important demographic and geographic information is lacking in most health departments nationwide. The Department of Defense Global Emerging Infections System sponsored a meeting and workshop in May 2000 in which participants discussed prototype systems and developed recommendations for new surveillance systems. The authors provide a summary of the group's findings, including expectations and recommendations for new surveillance systems. The consensus of the group was that a nationally led effort in developing health indicator surveillance methods is needed to promote effective, innovative systems.