An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems

In this paper, an experimental comparison between fixed and trained fusion rules for multimodal personal identity verification is reported. We focused on the behaviour of the considered fusion methods for ensembles of classifiers exhibiting significantly different performance, as this is one of the main characteristics of multimodal biometrics systems. The experiments were carried out on the XM2VTS database, using eight experts based on speech and face data. As fixed fusion methods, we considered the sum, majority voting, and order statistics based rules. The considered trained methods are the Behaviour Knowledge Space and the weighted averaging of classifiers outputs.

• Type: Journal
• Year: 2002
• Where: MCS
• Authors: Fabio Roli, Josef Kittler, Giorgio Fumera, Daniele Muntoni
• Keywords: Fusion Methods, MCS 2002, Multimodal Biometrics Systems, Multimodal Personal Identity, Pattern Recognition