An adaptive model for multi-modal biometrics decision fusion

Abstract

Multi-modal biometric verification is gaining more and more attention recently because of the high security level it provides and the non-universality of uni-modal biometrics. Multi-modal biometrics decision fusion can be considered as a classification task since the output is either a genuine user or an impostor. This treatment allows many available classifiers to be applied in the field. In this thesis, two problems related to multi-modal biometrics decision fusion are considered. The first problem is new user registration. Frequent registration not only requires storing of new patterns into the biometric database but also requires updating the combination module efficiently. The second problem is related to sensor decay which results in change of matching scores with time. The performance of a fixed classifier may be affected for such case. In this thesis, an adaptive algorithm to solve these problems has been proposed. This algorithm can update the combination module whenever new training patterns are available without having to retrain the module from scratch. The new algorithm is demonstrated using experiments on physical application data to address both the registration and matching scores distribution changing problems using three biometrics, namely fingerprint, speech and hand-geometry.