Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model
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,...
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MDPI AG
2015-08-01
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Online Access: | http://www.mdpi.com/2078-2489/6/3/494 |
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author | Khalid Fakhar Mohamed El Aroussi Mohamed Nabil Saidi Driss Aboutajdine |
author_facet | Khalid Fakhar Mohamed El Aroussi Mohamed Nabil Saidi Driss Aboutajdine |
author_sort | Khalid Fakhar |
collection | DOAJ |
description | 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. |
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institution | Directory Open Access Journal |
issn | 2078-2489 |
language | English |
last_indexed | 2024-04-12T02:38:51Z |
publishDate | 2015-08-01 |
publisher | MDPI AG |
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spelling | doaj.art-7959322b1ac84fedb896aee8e70c933f2022-12-22T03:51:23ZengMDPI AGInformation2078-24892015-08-016349450410.3390/info6030494info6030494Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification ModelKhalid Fakhar0Mohamed El Aroussi1Mohamed Nabil Saidi2Driss Aboutajdine3LRIT Research Laboratory (associated unit to CNRST, URAC no. 29), Faculty of Sciences, Mohammed V University, Rabat 10 000, MoroccoLRIT Research Laboratory (associated unit to CNRST, URAC no. 29), Faculty of Sciences, Mohammed V University, Rabat 10 000, MoroccoLRIT Research Laboratory (associated unit to CNRST, URAC no. 29), Faculty of Sciences, Mohammed V University, Rabat 10 000, MoroccoLRIT Research Laboratory (associated unit to CNRST, URAC no. 29), Faculty of Sciences, Mohammed V University, Rabat 10 000, MoroccoThis 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.http://www.mdpi.com/2078-2489/6/3/494multibiometricsidentificationscore level fusionfuzzy integralsfuzzy measures |
spellingShingle | Khalid Fakhar Mohamed El Aroussi Mohamed Nabil Saidi Driss Aboutajdine Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model Information multibiometrics identification score level fusion fuzzy integrals fuzzy measures |
title | Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model |
title_full | Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model |
title_fullStr | Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model |
title_full_unstemmed | Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model |
title_short | Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model |
title_sort | applying the upper integral to the biometric score fusion problem in the identification model |
topic | multibiometrics identification score level fusion fuzzy integrals fuzzy measures |
url | http://www.mdpi.com/2078-2489/6/3/494 |
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