Fuad M. Alkoot

Learn More
: The performance of a multiple classifier system combining the soft outputs of k-Nearest Neighbour (k-NN) Classifiers by the product rule can be degraded by the veto effect. This phenomenon is caused by k-NN classifiers estimating the class a posteriori probabilities using the maximum likelihood method. We show that the problem can be minimised by(More)
Amidst the conflicting experimental evidence of superiority of one over the other, we investigate the Sum and majority Vote combining rules in a two class case, under the assumption of experts being of equal strength and estimation errors conditionally independent and identically distributed. We show, analytically, that, for Gaussian estimation error(More)
Video-based biometric systems are becoming feasible thanks to advancement in both algorithms and computation platforms. Such systems have many advantages: improved robustness to spoof attack, performance gain thanks to variance reduction, and increased data quality/resolution, among others. We investigate a discriminative video-based score-level fusion(More)
We propose a novel design philosophy for expert fusion by taking the view that the design of individual experts and fusion cannot be solved in isolation. Each expert is constructed as part of the global design of a final multiple expert system. The design process involves jointly adding new experts to the multiple expert architecture and adding new features(More)
We propose a new fusion rule referred to as Modified Product (MProduct) and investigate its dependence on its only parameter, the truncation threshold. We show that MProduct classification rate peaks at a certain threshold value. At its optimal operating point MProduct outperforms Sum and Product. 2002 Elsevier Science B.V. All rights reserved.