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Booz | Allen | Hamilton HOLISTIC EVALUATION OF MULTI-BIOMETRIC SYSTEMS

As large scale biometric systems have been deployed throughout the landscape of the federal government, a new set of challenges and expectations have arisen. The ability to address increasing database sizes, demands on throughput, and privacy / legal issues are just a few examples of challenges at the forefront. Despite these challenges, the large scale systems are expected to continue to improve in terms of accuracy. In response to such challenges and expectations, systems relying on a single source of biometric input are transitioning to multi-biometric implementations. Traditional motivations behind the application of multi-biometric systems have centered on improving recognition performance, increasing population coverage, deterring spoof attacks, and reducing failure-to-enroll rates. The potential for improvement associated with these motivations is typically thought to come at the expense of increased processing time and computational complexity. In this paper, we provide supporting arguments for expanding the basis of multi-biometric system evaluation to reflect a holistic scope. Namely, the best multi-biometrics systems will maximize the degree in which requirements corresponding to the characteristics of universality, uniqueness, permanence, measurability, performance, acceptability, and circumvention are met. Additionally, we will demonstrate how evaluating the requirements of large scale federal multi-biometric systems necessitate an increased level of granularity beyond the traditional seven characteristics. Finally, we provide an example prioritization scheme which demonstrates how the overall measure of success need not uniformly consider all characteristics.