On the Relevance of Image Acquisition Resolution for Hand Geometry Identification Based on MLP

  • Authors:

  • Miguel A. Ferrera;Joan Fàbregas;Marcos Faundez-Zanuy;Jesús B. Alonso;Carlos Travieso;Amparo Sacristan

  • Affiliations:

  • Universidad de Las Palmas de Gran Canaria, Departamento de Seòales y Comunicaciones, Centro Tecnológico para la Innovación en Comunicaciones, Spain;Escola Universitària Politècnica de Mataró, Spain;Escola Universitària Politècnica de Mataró, Spain;Universidad de Las Palmas de Gran Canaria, Departamento de Seòales y Comunicaciones, Centro Tecnológico para la Innovación en Comunicaciones, Spain;Universidad de Las Palmas de Gran Canaria, Departamento de Seòales y Comunicaciones, Centro Tecnológico para la Innovación en Comunicaciones, Spain;Escola Universitària Politècnica de Mataró, Spain

  • Venue:
  • Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
  • Year:
  • 2009

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Abstract

The effect of changing the image resolution over a biometric system based on hand geometry is analyzed in this paper. Image resolution is progressively diminished from an initial 120dpi resolution up to 24dpi. The robustness of the examined system is analyzed with 2 databases and two identifiers. The first database acquires the images of the hand underneath whereas the second database acquires the images over the hand. The first classifier identifies with a multiclass support vector machine whereas the second classifier identifies with a neural network with error correction output codes. The four experiments show that an image resolution of 72dpi offers a good trade-off between performance and image resolution for the 15 geometric features used.