Combining multiple biometric traits with an order-preserving score fusion algorithm

Multibiometric systems based on score fusion can effectively combine the discriminative power of multiple biometric traits and overcome the limitations of individual trait, leading to a better performance of biometric authentication. To tackle multiple adverse issues with the established classifier-based or probability-based algorithms, in this paper we propose a novel order-preserving probabilistic score fusion algorithm, Order-Preserving Tree (OPT), by casting the score fusion problem into an optimisation problem with the natural order-preserving constraint. OPT is an algorithm fully non-parametric and widely applicable, not assuming any parametric forms of probabilities or independence among sources, directly estimating the posterior probabilities from maximum likelihood estimation, and exploiting the power of tree-structured ensembles. We demonstrate the effectiveness of our OPT algorithm by comparing it with many widely used score fusion algorithms on two prevalent multibiometric databases. HighlightsWe propose a probabilistic score fusion algorithm.The algorithm is based on the order-preserving constraints.The algorithm is fully non-parametric with no hyper-parameters to be tuned.A tree-structured ensemble is used to avoid the dimensionality curse.Experiments on two databases show the effectiveness of the algorithm.