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

Abstract

Cite this article as: Yicong Liang, Xiaoqing Ding, Changsong Liu, Jing-Hao Xue, Combining multiple biometric traits with an order-preserving score fusion algorithm, Neurocomputing, http://dx. AbstractMultibiometric 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 classifierbased 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.

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

“…Likelihood ratio achieves optimal performance at all operating points on receiver operating characteristics (ROCs), provided the estimation of probability density functions for genuine and impostor distributions are accurate. Parametric density estimation methods [18, 19] assume some predefined model for genuine and impostor scores distribution which is not always accurate, whereas non‐parametric methods [20, 21] require a large number of training data for estimating the underlying distribution which is an expensive process.…”