Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

Santos Sierra, Alberto de; Sánchez Ávila, Carmen; Bailador del Pozo, Gonzalo y Guerra Casanova, Javier (2011). Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices. En: "2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC)", 19/10/2011 - 21/10/2011, Salamanca, España. ISBN 978-1-4577-1122-0.

Descripción

Título: Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices
Autor/es:
  • Santos Sierra, Alberto de
  • Sánchez Ávila, Carmen
  • Bailador del Pozo, Gonzalo
  • Guerra Casanova, Javier
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC)
Fechas del Evento: 19/10/2011 - 21/10/2011
Lugar del Evento: Salamanca, España
Título del Libro: Proceeding of 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC)
Fecha: 2011
ISBN: 978-1-4577-1122-0
Materias:
Escuela: Centro de Domótica Integral (CeDInt) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

Más información

ID de Registro: 13544
Identificador DC: http://oa.upm.es/13544/
Identificador OAI: oai:oa.upm.es:13544
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6089603
Depositado por: Memoria Investigacion
Depositado el: 17 Oct 2012 09:22
Ultima Modificación: 21 Abr 2016 12:51
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