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

  • Ricardo García-Ródenas
  • Luis Jiménez Linares
  • Julio Alberto López-Gómez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614)


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.


Deep neural networks Convolutional neural networks Face recognition Object recognition 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ricardo García-Ródenas
    • 1
  • Luis Jiménez Linares
    • 1
  • Julio Alberto López-Gómez
    • 1
  1. 1.Department of MathematicsUniversity of Castilla la ManchaCiudad RealSpain

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