Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Santos Sierra, Alberto de; Sánchez Ávila, Carmen; Guerra Casanova, Javier y Bailador del Pozo, Gonzalo (2011). Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications. En: "International Conference on Signal Processing and Multimedia Applications (SIGMAP) 2011", 18/07/2011 - 21/07/2011, Sevilla, España.

Descripción

Título: Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications
Autor/es:
  • Santos Sierra, Alberto de
  • Sánchez Ávila, Carmen
  • Guerra Casanova, Javier
  • Bailador del Pozo, Gonzalo
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: International Conference on Signal Processing and Multimedia Applications (SIGMAP) 2011
Fechas del Evento: 18/07/2011 - 21/07/2011
Lugar del Evento: Sevilla, España
Título del Libro: Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP) 2011
Fecha: 2011
Materias:
Escuela: Centro de Domótica Integral (CeDInt) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance

Más información

ID de Registro: 13478
Identificador DC: http://oa.upm.es/13478/
Identificador OAI: oai:oa.upm.es:13478
Depositado por: Memoria Investigacion
Depositado el: 18 Oct 2012 09:21
Ultima Modificación: 21 Abr 2016 12:48
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