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Finger Vein Recognition Based on Local Directional Code

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the...

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Detalles Bibliográficos
Autores principales: Meng, Xianjing, Yang, Gongping, Yin, Yilong, Xiao, Rongyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522947/
https://www.ncbi.nlm.nih.gov/pubmed/23202194
http://dx.doi.org/10.3390/s121114937
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author Meng, Xianjing
Yang, Gongping
Yin, Yilong
Xiao, Rongyang
author_facet Meng, Xianjing
Yang, Gongping
Yin, Yilong
Xiao, Rongyang
author_sort Meng, Xianjing
collection PubMed
description Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.
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spelling pubmed-35229472013-01-09 Finger Vein Recognition Based on Local Directional Code Meng, Xianjing Yang, Gongping Yin, Yilong Xiao, Rongyang Sensors (Basel) Article Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP. Molecular Diversity Preservation International (MDPI) 2012-11-05 /pmc/articles/PMC3522947/ /pubmed/23202194 http://dx.doi.org/10.3390/s121114937 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Meng, Xianjing
Yang, Gongping
Yin, Yilong
Xiao, Rongyang
Finger Vein Recognition Based on Local Directional Code
title Finger Vein Recognition Based on Local Directional Code
title_full Finger Vein Recognition Based on Local Directional Code
title_fullStr Finger Vein Recognition Based on Local Directional Code
title_full_unstemmed Finger Vein Recognition Based on Local Directional Code
title_short Finger Vein Recognition Based on Local Directional Code
title_sort finger vein recognition based on local directional code
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522947/
https://www.ncbi.nlm.nih.gov/pubmed/23202194
http://dx.doi.org/10.3390/s121114937
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