Multimodal decision-level fusion for person authentication

V. Chatzis, A.G. Bors, I. Pitas

Research output: Contribution to journalArticlepeer-review


In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization (FVQ) algorithms, and median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a novel fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system.
Original languageEnglish
Pages (from-to)674-680
Number of pages6
JournalIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans
Issue number6
Publication statusPublished - Nov 1999

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  • Data fusion
  • fuzzy clustering
  • fuzzy logic
  • median RBF
  • person authentication.

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