Decision-Level Fusion Strategies for Correlated Biometric Classifiers
The focus of this paper is on designing decision-level fusion strategies for correlated biometric classifiers. In this regard, two different strategies are investigated. In the first strategy, an optimal fusion rule based on the Likelihood Ratio Test (LRT) and the Chair Varshney Rule (CVR) is discussed for correlated hypothesis testing where the thresholds of the individual biometric classifiers are first fixed. In the second strategy, a Particle Swarm Optimization (PSO) based procedure is proposed to simultaneously optimize the thresholds and the fusion rule.