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Vein biometric recognition on a smartphone

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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.accessioned2021-12-20T11:20:55Z
dc.date.available2021-12-20T11:20:55Z
dc.date.issued2020-06-04
dc.descriptionTopic: Intelligent Biometric Systems for Secure Societies.en
dc.description.abstractHuman recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices.en
dc.format.extent13
dc.identifier.bibliographicCitationGarcia-Martin, R. & Sanchez-Reillo, R. (2020). Vein Biometric Recognition on a Smartphone. IEEE Access, 8, 104801–104813.en
dc.identifier.doihttps://doi.org/10.1109/access.2020.3000044
dc.identifier.issn2169-3536
dc.identifier.publicationfirstpage104801
dc.identifier.publicationlastpage104813
dc.identifier.publicationtitleIEEE Accessen
dc.identifier.publicationvolume8
dc.identifier.urihttps://hdl.handle.net/10016/33802
dc.identifier.uxxiAR/0000028866
dc.language.isoeng
dc.publisherIEEEen
dc.rights© The authors, 2020. This work is licensed under a Creative Commons Attribution 4.0 License.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaElectrónicaes
dc.subject.otherVein biometric recognitionen
dc.subject.otherSmartphoneen
dc.subject.otherWrist vascular biometric recognitionen
dc.subject.otherContactless databaseen
dc.subject.otherBiometrics on mobile devicesen
dc.subject.otherNear-infrared cameraen
dc.subject.otherXiaomi© pocophone F1en
dc.subject.otherXiaomi© Mi 8en
dc.subject.otherSift® (scale-invariant feature transform)en
dc.subject.otherSurf® (speeded up robust features)en
dc.titleVein biometric recognition on a smartphoneen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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