Exploring Margin Maximization for Biometric Score Fusion

Biometric systems are automated methods based on physical or behavioral characteristics of an individual for determining her/his identity. An important aspect of these systems is the reliability against forgery that is surely improved when using multiple sources of biometric information. In such cases combination rules can be applied to fuse the different scores thus obtaining a multibiometric system. In this paper we analyze a method based on margin maximization for building a linear combination of biometric scores. The margin is a central concept in machine learning research and several theoretical results exist which show that improving the margin on the training set is beneficial for the generalization error of an ensemble of classifiers. Experiments performed on real biometric data and comparisons with other commonly employed fusion rules show that a combination based on margin maximization is particularly effective with respect to other established fusion methods.