Multibiometric Systems: Overview, Case Studies, and Open Issues

Information fusion refers to the reconciliation of evidence presented by multiple sources of information in order to generate a decision. In the context of biometrics, evidence reconciliation plays a pivotal role in enhancing the recognition accuracy of human authentication systems and is referred to as multibiometrics. Multibiometric systems combine the information presented by multiple biometric sensors, algorithms, samples, units, or traits in order to establish the identity of an individual. Besides enhancing matching performance, these systems are expected to improve population coverage, deter spoofing, facilitate continuous monitoring, and impart fault tolerance to biometric applications. This chapter introduces the topic of multibiometrics and enumerates the various sources of biometric information that can be consolidated as well as the different levels of fusion that are possible in a biometric system. The role of using ancillary information such as biometric data quality and soft biometric traits (e.g., height) to enhance the performance of these systems is discussed. Three case studies demonstrating the benefits of a multibiometric system and the factors impacting its architecture are also presented. Finally, some of the open challenges in multibiometric system design and implementation are enumerated.

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