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

• We 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.

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.