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• W118574294 abstract "Identity verification systems that use a mono modal biometrics always have tocontend with sensor noise and limitations of feature extractor and matching.However combining information from different biometrics modalities may wellprovide higher and more consistent performance levels. A robust yet simple schemecan fuse the decisions produced by the individual biometric experts. In this paper, k-Nearest Neighbourhood (k-NN) based classifiers are adopted in the decision fusionmodule for the face and speech experts. k-NN rule owes much of its popularity inpattern recognition community to its simplicity and good performance in practicalapplication. Besides that, k-NN may also provide a ternary decision scheme which israrely found in other classifiers. The fusion decision schemes considered are voting-,modified- and theoretic evidence of k-NN classifiers based on Dempster-Shafertheory. The performances of these k-NN classifiers are evaluated in both balancedand unbalanced conditions and compared with other classification approaches suchas sum rule, voting techniques and Multilayer Perceptron on a bimodal database.Keywords: Biometrics, face verification, speaker verification, k-NN classifiers.CR Categories: I.5.4, I.5.1, I.4.9, J.0" @default.
• W118574294 created "2016-06-24" @default.
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• W118574294 date "2004-02-01" @default.
• W118574294 modified "2023-09-25" @default.
• W118574294 title "Nearest Neighbourhood Classifiers in a Bimodal Biometric Verification System Fusion Decision Scheme" @default.
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