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.

Several 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.

We have 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.

Keywords Likelihood Ratio; Mixture of Gaussians; Support Vector Machine; Biometric score fusion.

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