Biometric systems automatically measure a physiological or behavioral "signature" of an individual, from which a decision can be rendered to either authenticate or determine the identity of that individual. In this way, biometric systems provide the means to bind the physical presence of an individual user with her cyber action. Despite the increasing use of these systems, neither an analytic framework for modeling and predicting their performance, nor a set of standard multibiometric data is available to researchers for testing purposes.
This research addresses these deficiencies by determining an analytic framework in which the performance of biometric systems can be modeled and predicted, removing key barriers to biometric system performance through research in multibiometrics and effective vulnerability countermeasures, and understanding the relationships among biometric applications, privacy, security, and user acceptance that are essential for both informed public policy and system design. Addressing this interrelated set of key technical barriers and societal issues will advance the understanding of biometric system acceptance, performance, and design, and will enable trustworthy authentication and identification functions essential to pervasive computing and supporting homeland security needs. The data sets generated by the coordinated tasks of this research are also being made available as a resource to the biometrics research, developer, and user communities.
