SBIR-STTR Award

Rapid and accurate identification of all individuals has been the goal of the biometric industry for many years. Unfortunately, due to limitations in technology and variations in the biometric itself, no one biometric imaging modality has been able to satisfy the performance requirements of most large scale identity management applications. This research addresses the fundamental hypothesis of whether two or more biometric systems can be combined to deliver improved performance over a single biometric system. This work will develop the scientific testing methodology to combine multiple biometric measurements in order to achieve superior performance over a single biometric system. A general purpose biometric testing tool will be developed that will accept enrollment and inquiry data from multiple biometric imaging systems and generate the corresponding ROC performance curves. Upon successful completion of this initiative, system performance requirements can be presented in the form of Pfm, Pfnm, and throughput, and a metric driven approach taken to determine if those requirements are in fact, achievable. The system designer can then ascertain what types of biometric technology would be required to achieve the desired performance, and how the individual biometric technologies should be combined to meet the stated goal

The development of a rapid and accurate personal identification system improves our nation’s capability in identifying potential terrorists and provides the most reliable means for securing government or private industry’s confidential or classified secrets. However, single biometric based applications have not been widely deployed due to the variation in accuracy and their inability to prevent fraudulent attacks on the system. An Automated Multimodal Biometric Identification System (AMBIS) along with a multimodal biometric based Identity Management Portal will be developed under this initiative, tunable to enable rapid and much more accurate identification of individuals in two typical operations

Keywords:
Biometrics, Multimodal Biometrics, Identity Management, Biometric Fusion, Automatic Biometric Identi