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A Multi-Biometric System Based on Feature and Score Level Fusions

Title:

A Multi-Biometric System Based on Feature and Score Level Fusions

Kabir, Waziha, Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 and Swamy, M. N. S. ORCID: https://orcid.org/0000-0002-3989-5476 (2019) A Multi-Biometric System Based on Feature and Score Level Fusions. IEEE Access, 7 . pp. 59437-59450. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2914992

Abstract

In general, the information of multiple biometric modalities is fused at a single level, for example, score level or feature level. The recognition accuracy of a multimodal biometric system may not be improved by carrying fusion at a single level, since one matcher may provide a performance lower than that provided by other matchers. In view of this, we propose a new fusion scheme, referred to as the matcher performance-based (MPb) fusion scheme, in which the fusion is carried out at two levels, feature level, and score level, to improve the overall recognition accuracy. First, we consider the performance of the individual matchers in order to find out which of the modalities should be used for fusion at the feature level. Then, the selected modalities are fused at this level by utilizing their encoded features. Next, we fuse the score obtained from the feature-level fusion with that of the modality for which the performance is the highest. In order to carry out this fusion, a new normalization technique referred to as the overlap extrema-variation-based anchored min-max (OEVBAMM) normalization technique, is also proposed. By considering three modalities, namely, fingerprint, palmprint, and earprint, the performance of the proposed fusion scheme as well as that of the single level fusion scheme, both with various normalization and weighting techniques are evaluated in terms of a number of metrics. It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Kabir, Waziha and Ahmad, M. Omair and Swamy, M. N. S.
Journal or Publication:IEEE Access
Date:2019
Funders:
  • Concordia Open Access Author Fund
  • Natural Sciences and Engineering Research Council (NSERC) of Canada
  • Regroupement Stratégique en Microélectronique du Québec (ReSMiQ)
Digital Object Identifier (DOI):10.1109/ACCESS.2019.2914992
Keywords:Biometrics, feature level fusion, multi-biometric system, normalization, score level fusion
ID Code:986013
Deposited By: KRISTA ALEXANDER
Deposited On:04 Oct 2019 19:45
Last Modified:04 Oct 2019 19:45

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