Optimal Face-Iris Multimodal Fusion Scheme

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 extrac...
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Autor*in:	Omid Sharifi [verfasserIn] Maryam Eskandari [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	multimodal biometrics Backtracking Search Algorithm match score level fusion feature level fusion decision level fusion optimization

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 8(2016), 6, p 48
Übergeordnetes Werk:	volume:8 ; year:2016 ; number:6, p 48

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym8060048

Katalog-ID:	DOAJ079133967

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language	English
source	In Symmetry 8(2016), 6, p 48 volume:8 year:2016 number:6, p 48
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institution	findex.gbv.de
topic_facet	multimodal biometrics Backtracking Search Algorithm match score level fusion feature level fusion decision level fusion optimization Mathematics
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container_title	Symmetry
authorswithroles_txt_mv	Omid Sharifi @@aut@@ Maryam Eskandari @@aut@@
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callnumber-first	Q - Science
author	Omid Sharifi
spellingShingle	Omid Sharifi misc QA1-939 misc multimodal biometrics misc Backtracking Search Algorithm misc match score level fusion misc feature level fusion misc decision level fusion misc optimization misc Mathematics Optimal Face-Iris Multimodal Fusion Scheme
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topic_title	QA1-939 Optimal Face-Iris Multimodal Fusion Scheme multimodal biometrics Backtracking Search Algorithm match score level fusion feature level fusion decision level fusion optimization
topic	misc QA1-939 misc multimodal biometrics misc Backtracking Search Algorithm misc match score level fusion misc feature level fusion misc decision level fusion misc optimization misc Mathematics
topic_unstemmed	misc QA1-939 misc multimodal biometrics misc Backtracking Search Algorithm misc match score level fusion misc feature level fusion misc decision level fusion misc optimization misc Mathematics
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title	Optimal Face-Iris Multimodal Fusion Scheme
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title_sort	optimal face-iris multimodal fusion scheme
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title_auth	Optimal Face-Iris Multimodal Fusion Scheme
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Optimal Face-Iris Multimodal Fusion Scheme
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