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Information 2015, 6(3), 494-504; doi:10.3390/info6030494

Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model

1
LRIT Research Laboratory (associated unit to CNRST, URAC no. 29), Faculty of Sciences, Mohammed V University, Rabat 10 000, Morocco
2
LETI, Ecole Hassania des Travaux Publics, Casablanca, 20 000, Morocco
3
Institut National de Statistique et d’Economie Appliquée, Rabat, 10 000, Morocco
*
Author to whom correspondence should be addressed.
Academic Editor: Ahmed El Oualkadi
Received: 10 June 2015 / Revised: 3 August 2015 / Accepted: 3 August 2015 / Published: 14 August 2015
(This article belongs to the Special Issue Selected Papers from MedICT 2015)
View Full-Text   |   Download PDF [262 KB, uploaded 14 August 2015]   |  

Abstract

This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno’s fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach. View Full-Text
Keywords: multibiometrics; identification; score level fusion; fuzzy integrals; fuzzy measures multibiometrics; identification; score level fusion; fuzzy integrals; fuzzy measures
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fakhar, K.; Aroussi, M.E.; Saidi, M.N.; Aboutajdine, D. Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model. Information 2015, 6, 494-504.

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