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Wrist vascular biometric recognition using a portable contactless system

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dc.affiliation.dptoUC3M. Departamento de Tecnología Electrónicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Universitario de Tecnologías de Identificación (GUTI)es
dc.contributor.authorGarcía-Martín, Raúl
dc.contributor.authorSanchez-Reillo, Raul
dc.date.accessioned2021-12-15T10:00:48Z
dc.date.available2021-12-15T10:00:48Z
dc.date.issued2020-03-07
dc.description.abstractHuman wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.en
dc.description.statusPublicadoes
dc.format.extent20
dc.identifier.bibliographicCitationSensors 2020, 20(5), 1469, pp.: 1-20.en
dc.identifier.doihttps://doi.org/10.3390/s20051469
dc.identifier.issn1424-8220
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue5(1469)
dc.identifier.publicationlastpage20
dc.identifier.publicationtitleSENSORSen
dc.identifier.publicationvolume20
dc.identifier.urihttps://hdl.handle.net/10016/33767
dc.identifier.uxxiAR/0000025626
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2020 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaElectrónicaes
dc.subject.otherContactless dataseten
dc.subject.otherIdentificationen
dc.subject.otherInfrared (IR) camerasen
dc.subject.otherNon-contact devicesen
dc.subject.otherOriented FAST and Rotated BRIEF (ORB)en
dc.subject.otherScale-Invariant Feature Transform (SIFT®)en
dc.subject.otherSpeeded Up Robust Features (SURF®)en
dc.subject.otherWrist vein recognitionen
dc.subject.otherVascular biometric recognitionen
dc.titleWrist vascular biometric recognition using a portable contactless systemen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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