Towards hand biometrics in mobile devices

Santos Sierra, Alberto de; Sánchez Ávila, Carmen; Guerra Casanova, Javier y Mendaza Ormaza, Aitor (2011). Towards hand biometrics in mobile devices. En: "BIOSIG 2011", 08/09/2011 - 09/09/2011, Darmstadt, Alemania. ISBN 978-3-88579-285-7. pp. 203-210.


Título: Towards hand biometrics in mobile devices
  • Santos Sierra, Alberto de
  • Sánchez Ávila, Carmen
  • Guerra Casanova, Javier
  • Mendaza Ormaza, Aitor
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: BIOSIG 2011
Fechas del Evento: 08/09/2011 - 09/09/2011
Lugar del Evento: Darmstadt, Alemania
Título del Libro: Proceedings of BIOSIG 2011
Fecha: 2011
ISBN: 978-3-88579-285-7
Escuela: Centro de Domótica Integral (CeDInt) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger)

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Depositado el: 17 Oct 2012 11:23
Ultima Modificación: 21 Abr 2016 12:50
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