A multi-modal dataset, protocol and tools for adaptive biometric systems: A benchmarking study

Adaptive biometric systems have received a recent spurt in biometric community. These systems have the additional capability to adapt themselves using biometric data available during the system's operation. Although several studies have been proposed in this field, no conclusive evidences can be drawn about the expected performance gain on making the biometric system adaptive. This is due to the adoption of different and inappropriate databases, protocols and tools for evaluating adaptive biometric systems. This paper presents a benchmarking study to facilitate fair comparison and independent replication of the results from different research groups. To this aim, this paper describes DIEE multi-modal database consisting of face and fingerprint biometrics and a protocol tailored for adapting as well as evaluating adaptive biometric systems. In addition, several tools for evaluating and visualising the performance gain on making the biometric system adaptive are provided as well. To the best of our knowledge, this is the first attempt to benchmark database, protocol and tools for evaluating adaptive biometric systems operating in verification mode