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The Effects of Regularization on Learning Facial Expressions with Convolutional Neural Networks

  • Tobias HinzEmail author
  • Pablo Barros
  • Stefan Wermter
Conference paper
  • 2.6k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)

Abstract

Convolutional neural networks (CNNs) have become effective instruments in facial expression recognition. Very good results can be achieved with deep CNNs possessing many layers and providing a good internal representation of the learned data. Due to the potentially high complexity of CNNs on the other hand they are prone to overfitting and as a result, regularization techniques are needed to improve the performance and minimize overfitting. However, it is not yet clear how these regularization techniques affect the learned representation of faces. In this paper we examine the effects of novel regularization techniques on the training and performance of CNNs and their learned features. We train a CNN using dropout, max pooling dropout, batch normalization and different combinations of these three. We show that a combination of these methods can have a big impact on the performance of a CNN, almost halving its validation error. A visualization technique is applied to the CNNs to highlight their activations for different inputs, illustrating a significant difference between a standard CNN and a regularized CNN.

Keywords

Convolutional neural network Facial expression recognition Regularization Batch normalization Dropout Max pooling dropout 

Notes

Acknowledgments

This work was partially supported by the CAPES Brazilian Federal Agency for the Support and Evaluation of Graduate Education (p.n.5951–13–5), the German Research Foundation DFG under project CML (TRR 169), and the Hamburg Landesforschungsförderungsprojekt.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HamburgHamburgGermany

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