Score-Level Fusion by Generalized Delaunay Triangulation

This paper describes a method for score-level fusion in multi-cue two-class classification problems. Fusion based on the probability density function (PDF) of multiple scores given for each class is a promising approach because it guarantees optimality as long as the estimated PDFs are correct. Instead of lattice-type control points used in previous non-parametric density-based approaches, floating control points (FCPs) are introduced to improve scalability and the whole posterior distribution is represented by interpolation or extrapolation using generalized Delaunay triangulation. Given a set of FCPs obtained by k-means, posteriors on the FCPs are estimated by an energy minimization framework using training samples. The experiments, using both simulation data as well as several types of real data from three publicly available score databases for multi-cue biometric authentication, demonstrate the effectiveness of the proposed method.