Real-Time Dorsal Hand Recognition Based on Smartphone

The integration of biometric recognition with smartphones is necessary to increase security, especially in financial transactions such as online payments. Vein recognition of the dorsal hand is superior to other methods such as palm, finger, and wrist, as it has a wide area to be captured and does n...

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Autores principales:	Mohamed I. Sayed, Mohamed Taha, Hala H. Zayed
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Biometric system dorsal hand features extraction features matching near-infrared veins recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4ddbbd931e394bc180ad6b472462bd5b
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id	oai:doaj.org-article:4ddbbd931e394bc180ad6b472462bd5b
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spelling	oai:doaj.org-article:4ddbbd931e394bc180ad6b472462bd5b2021-11-17T00:00:57ZReal-Time Dorsal Hand Recognition Based on Smartphone2169-353610.1109/ACCESS.2021.3126709https://doaj.org/article/4ddbbd931e394bc180ad6b472462bd5b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606904/https://doaj.org/toc/2169-3536The integration of biometric recognition with smartphones is necessary to increase security, especially in financial transactions such as online payments. Vein recognition of the dorsal hand is superior to other methods such as palm, finger, and wrist, as it has a wide area to be captured and does not have any wrinkles. Most current systems that depend on dorsal hand vein recognition do not work in real-time and have poor results. In this paper, a dorsal hand recognition system working in real-time is proposed to achieve good results with a high frame rate. A contactless device consists of a universal serial bus (USB)camera and infrared LEDs and is connected to a smartphone used to collect our dataset. The dataset contained 2200 images collected from both hands of 100 individuals. The captured images were processed with light algorithms to improve the real-time performance and increase the frame rate. The feature detection and extraction algorithm is oriented FAST and rotated BRIEF (ORB) with K-nearest neighbors (K-NN) matching to match features. Another benchmark, called the Poznan University of Technology (PUT) dataset, is used to measure the efficiency of the proposed system. The results obtained from experimental testing showed that the proposed system had a low equal error rate (EER) of 4.33% and a high frame rate of 29 frames per second.Mohamed I. SayedMohamed TahaHala H. ZayedIEEEarticleBiometric systemdorsal handfeatures extractionfeatures matchingnear-infraredveins recognitionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151118-151128 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biometric system dorsal hand features extraction features matching near-infrared veins recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Biometric system dorsal hand features extraction features matching near-infrared veins recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohamed I. Sayed Mohamed Taha Hala H. Zayed Real-Time Dorsal Hand Recognition Based on Smartphone
description	The integration of biometric recognition with smartphones is necessary to increase security, especially in financial transactions such as online payments. Vein recognition of the dorsal hand is superior to other methods such as palm, finger, and wrist, as it has a wide area to be captured and does not have any wrinkles. Most current systems that depend on dorsal hand vein recognition do not work in real-time and have poor results. In this paper, a dorsal hand recognition system working in real-time is proposed to achieve good results with a high frame rate. A contactless device consists of a universal serial bus (USB)camera and infrared LEDs and is connected to a smartphone used to collect our dataset. The dataset contained 2200 images collected from both hands of 100 individuals. The captured images were processed with light algorithms to improve the real-time performance and increase the frame rate. The feature detection and extraction algorithm is oriented FAST and rotated BRIEF (ORB) with K-nearest neighbors (K-NN) matching to match features. Another benchmark, called the Poznan University of Technology (PUT) dataset, is used to measure the efficiency of the proposed system. The results obtained from experimental testing showed that the proposed system had a low equal error rate (EER) of 4.33% and a high frame rate of 29 frames per second.
format	article
author	Mohamed I. Sayed Mohamed Taha Hala H. Zayed
author_facet	Mohamed I. Sayed Mohamed Taha Hala H. Zayed
author_sort	Mohamed I. Sayed
title	Real-Time Dorsal Hand Recognition Based on Smartphone
title_short	Real-Time Dorsal Hand Recognition Based on Smartphone
title_full	Real-Time Dorsal Hand Recognition Based on Smartphone
title_fullStr	Real-Time Dorsal Hand Recognition Based on Smartphone
title_full_unstemmed	Real-Time Dorsal Hand Recognition Based on Smartphone
title_sort	real-time dorsal hand recognition based on smartphone
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4ddbbd931e394bc180ad6b472462bd5b
work_keys_str_mv	AT mohamedisayed realtimedorsalhandrecognitionbasedonsmartphone AT mohamedtaha realtimedorsalhandrecognitionbasedonsmartphone AT halahzayed realtimedorsalhandrecognitionbasedonsmartphone
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Real-Time Dorsal Hand Recognition Based on Smartphone

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