A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does n...
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Autor*in:	Fu Liu [verfasserIn] Shoukun Jiang [verfasserIn] Bing Kang [verfasserIn] Tao Hou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Dorsal hand vein recognition biometric graph matching occlusion databases

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 74525-74534
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:74525-74534

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2988714

Katalog-ID:	DOAJ058858547

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spelling	10.1109/ACCESS.2020.2988714 doi (DE-627)DOAJ058858547 (DE-599)DOAJb012ff2d84e342d1911f34ef8065b126 DE-627 ger DE-627 rakwb eng TK1-9971 Fu Liu verfasserin aut A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands. Dorsal hand vein recognition biometric graph matching occlusion databases Electrical engineering. Electronics. Nuclear engineering Shoukun Jiang verfasserin aut Bing Kang verfasserin aut Tao Hou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 74525-74534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:74525-74534 https://doi.org/10.1109/ACCESS.2020.2988714 kostenfrei https://doaj.org/article/b012ff2d84e342d1911f34ef8065b126 kostenfrei https://ieeexplore.ieee.org/document/9072173/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 74525-74534
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allfieldsSound	10.1109/ACCESS.2020.2988714 doi (DE-627)DOAJ058858547 (DE-599)DOAJb012ff2d84e342d1911f34ef8065b126 DE-627 ger DE-627 rakwb eng TK1-9971 Fu Liu verfasserin aut A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands. Dorsal hand vein recognition biometric graph matching occlusion databases Electrical engineering. Electronics. Nuclear engineering Shoukun Jiang verfasserin aut Bing Kang verfasserin aut Tao Hou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 74525-74534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:74525-74534 https://doi.org/10.1109/ACCESS.2020.2988714 kostenfrei https://doaj.org/article/b012ff2d84e342d1911f34ef8065b126 kostenfrei https://ieeexplore.ieee.org/document/9072173/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 74525-74534
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source	In IEEE Access 8(2020), Seite 74525-74534 volume:8 year:2020 pages:74525-74534
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topic_facet	Dorsal hand vein recognition biometric graph matching occlusion databases Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Fu Liu @@aut@@ Shoukun Jiang @@aut@@ Bing Kang @@aut@@ Tao Hou @@aut@@
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callnumber-first	T - Technology
author	Fu Liu
spellingShingle	Fu Liu misc TK1-9971 misc Dorsal hand vein recognition misc biometric graph matching misc occlusion misc databases misc Electrical engineering. Electronics. Nuclear engineering A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching
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topic_title	TK1-9971 A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching Dorsal hand vein recognition biometric graph matching occlusion databases
topic	misc TK1-9971 misc Dorsal hand vein recognition misc biometric graph matching misc occlusion misc databases misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Dorsal hand vein recognition misc biometric graph matching misc occlusion misc databases misc Electrical engineering. Electronics. Nuclear engineering
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title	A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching
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title_full	A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching
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title_sort	recognition system for partially occluded dorsal hand vein using improved biometric graph matching
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title_auth	A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching
abstract	Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands.
abstractGer	Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands.
abstract_unstemmed	Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands.
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title_short	A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching
url	https://doi.org/10.1109/ACCESS.2020.2988714 https://doaj.org/article/b012ff2d84e342d1911f34ef8065b126 https://ieeexplore.ieee.org/document/9072173/ https://doaj.org/toc/2169-3536
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