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Symmetry 2016, 8(6), 48; https://doi.org/10.3390/sym8060048

Optimal Face-Iris Multimodal Fusion Scheme

Department of Computer and Software Engineering, Toros University, Mersin 33140, Turkey
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Author to whom correspondence should be addressed.
Academic Editor: Angel Garrido
Received: 9 May 2016 / Revised: 6 June 2016 / Accepted: 7 June 2016 / Published: 15 June 2016
(This article belongs to the Special Issue Symmetry in Complex Networks II)
Full-Text   |   PDF [3010 KB, uploaded 15 June 2016]   |  

Abstract

Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods. View Full-Text
Keywords: multimodal biometrics; Backtracking Search Algorithm; match score level fusion; feature level fusion; decision level fusion; optimization multimodal biometrics; Backtracking Search Algorithm; match score level fusion; feature level fusion; decision level fusion; optimization
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Sharifi, O.; Eskandari, M. Optimal Face-Iris Multimodal Fusion Scheme. Symmetry 2016, 8, 48.

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