Score Fusion by Maximizing the Area under the ROC Curve

Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impostor priors or the costs for the different errors. Empirical results on several datasets show the effectiveness of the proposed approach.

• Type: Conference
• Year: 2009
• Where: IBPRIA
• Authors: Mauricio Villegas, Roberto Paredes
• Keywords: Active Research Topic, Fusion Parameters, IBPRIA 2009, Pattern Recognition, Score Fusion Model