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Analyzing Wavelets Components to Perform Face Recognition

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Bio-Inspired Applications of Connectionism (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2085))

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Abstract

Face recognition is a very difficult task in real environments. In those cases a good preprocessing of the images is needed to keep the images invariant to translations, scales, luminosity, shape, aspect, rotation, noise, etc ... Wavelet transformation have been probed to be a good preprocessing method for many task. However, not all the coefficients of a wavelet transform have the information needed for a classification method to be efficient. This work introduce a method to select the most appropriate coefficients for a wavelet transform to allow an unsupervised neural network to well classify a set of complex faces.

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Author information

Authors and Affiliations

  1. Departamento de Informtica, Universidad Carlos III, Leganes, Madrid

    Pedro Isasi & Manuel Velasco

  2. Departamento de Lenguajes y Sistemas Informticos, Facultad de Informtica U.P.M, Boadilla del Monte, Madrid

    Javier Segovia

Authors
  1. Pedro Isasi
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  2. Manuel Velasco
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  3. Javier Segovia
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Senda del Rey, s/n., 28040, Madrid, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Isasi, P., Velasco, M., Segovia, J. (2001). Analyzing Wavelets Components to Perform Face Recognition. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_31

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  • DOI: https://doi.org/10.1007/3-540-45723-2_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42237-2

  • Online ISBN: 978-3-540-45723-7

  • eBook Packages: Springer Book Archive

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