Likelihood ratio based features for a trained biometric score fusion

In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step. l tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers. e tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost.