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On the Performance of Classic and Deep Neural Models in Image Recognition

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

Deep learning has arisen in the last years as a powerful and ultimate tool for machine learning problems. This article analyses the performance of classic and deep neural network models in a challenging problem like face recognition. The aim of this article is to study what the main advantages and disadvantages deep neural networks provide and when they will be more suitable than classic models, which have also obtained really good results in some complex problems. Is it worth using deep learning? The results show that deep models increase the learning capabilities of classic neural networks in problems with high non-linearities features.

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Acknowledgment

The authors would like to express his thanks to the project with number PEIC-2014- 003-P and to the authorities that give their support to its development, the FEDER and the Junta de Comunidades de Castilla la Mancha.

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Authors and Affiliations

  1. Department of Mathematics, University of Castilla la Mancha, Ciudad Real, Spain

    Ricardo García-Ródenas, Luis Jiménez Linares & Julio Alberto López-Gómez

Authors
  1. Ricardo García-Ródenas
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  2. Luis Jiménez Linares
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  3. Julio Alberto López-Gómez
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Corresponding author

Correspondence to Ricardo García-Ródenas .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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García-Ródenas, R., Linares, L.J., López-Gómez, J.A. (2017). On the Performance of Classic and Deep Neural Models in Image Recognition. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_68

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

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