Applying Advances in Biometric Fusion to Contextua

The use of contextual information is often a weak, error prone and labor intensive method of identifying and tracking Persons of Interest. Using the Neyman-Pearson Lemma, Ultra-Scan will fuse large numbers of historically weak contextual data fields to create accurate, high value identity information. The technical objective is to identify independent or weakly correlated contextual fields that can be treated as a score-based recognition system suitable for the Neyman Pearson Test, and which can then be used to significantly improve overall identification system performance. Phase I will research a large number of contextual personal identifiers that create an accurate form of personal identification when fused. The effort will create an ideal platform from which to implement a series of steps involving analysis, data modeling, estimation and software simulation to establish with mathematical certainty the ability to fuse large number of contextual fields to create a reliable form of identification.