The Biometric Technology Engine (BT-E) establishes an enduring core capability by leveraging S&T’s biometric expertise and ensuring the re-use of biometric tools, methods, and best practices, as well as support robust testing and evaluation at the Maryland Test Facility (MdTF) to inform applications of biometric technology to specific operational use cases across Apex programs, S&T programs, DHS, and the Homeland Security Enterprise (HSE).
Description and Approach
- Drive Efficiency: provide cross-cutting methods, best practices, and solutions to drive efficiencies across biometric programs;
- Address Needs: leverage combined capabilities of the Engine structure to address current, emerging, and future operational needs;
- Test and Evaluation: provide objective, first-class biometric testing and evaluation (T&E) services to Apex programs, S&T, DHS, and the HSE;
- Engage Industry: leverage combined industry insights and engage private sector to forage for innovative biometric solutions
- Encourage Innovation: drive biometric standards and innovation across the HSE.
The BT-E will accelerate effective integration of biometrics into operations and work in a cross-cutting fashion to mitigate operational inefficiencies.
- Coordinate with other Apex Engines and other S&T organizations to address Apex program needs
- Provide objective analysis of biometric technologies (i.e. strengths and weaknesses)
- Execute robust testing and evaluation at the MdTF to inform applications of biometric technology to specific operational use cases
- Formalize biometric technology evaluations to inform or streamline DHS technology acquisitions
- Identify common biometric capability gaps across DHS components and HSE stakeholders
- Leverage public and private sector expertise and relationships to identify innovative solutions that account for diverse demographics and human systems integration on the performance of biometric collection and matching processes.
The Maryland Test Facility (MdTF) provides a controlled, reconfigurable environment suitable for comparing and contrasting technologies, observing and documenting human-device interactions, and measuring the impact of process changes on system and/or human performance. Variables in human and environmental conditions can be incorporated so researchers can evaluate different technologies, capabilities, and operational processes under simulated, real-world conditions. This allows researchers to predict solution performance prior to deployment in an operational setting with a high degree of confidence.