Decision Level Fusion of Intramodal Personal Identity Verification Experts

Authors:
Josef Kittler;Marco Ballette;J. Czyz;Fabio Roli;L. Vandendorpe
Affiliations:
-;-;-;-;-
Venue:
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Year:
2002

Citing 4
Cited 1

A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals

IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Second International Workshop on Multiple Classifier Systems

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Information Analysis of Multiple Classifier Fusion

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Feature Subsets for Classifier Combination: An Enumerative Experiment

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems

An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination

Information Fusion

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the Behavior Knowledge Space [4] and Decision Templates [7] methods of classifier fusion in the context of personal identity verification involving six intramodal experts exploiting frontal face biometrics.The results of extensive experiments on the XM2VTS database show the Behavioural Knowledge Space fusion strategy achieves consistently better results than the Decision Templates method. Most importantly, it exhibits quasi monotonic behaviour as the number of experts combined increases.