Developing an Interoperable Contextual Fusion Platform

The use of contextual information is often a weak, error prone and labor intensive method of identifying and tracking Persons of Interest (POI). Ultra-Scan’s Phase I work successfully applied Neyman-Pearson fusion to weak contextual data, resulting in a powerful new algorithm that fuses discrete contextual data with biometrics in order to significantly improve system accuracy. Ultra-Scan’s algorithm actually increases performance as more weak data is fused. As a result of the Phase I effort, Ultra-Scan has established a solid foundation for the Phase II goal of developing a framework and methods for improving profile information and POI tracking, as well as constructing a prototype system to establish proof-of-concept. Upon completion of the Phase II effort, Ultra-Scan expects to deliver a prototype Interoperable Contextual Fusion Platform that will: Refine the company’s contextual fusion algorithm; Advance the fusion of contextual and biometric data for improved system accuracy; Create an automated, intuitive and customizable contextual mapping interface; Significantly improve the profiling of POIs for ID and tracking across disparate databases.