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Deep Learning for Emotion Recognition in Faces

  • Ariel Ruiz-GarciaEmail author
  • Mark Elshaw
  • Abdulrahman Altahhan
  • Vasile Palade
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)

Abstract

Deep Learning (DL) has shown real promise for the classification efficiency for emotion recognition problems. In this paper we present experimental results for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven emotions. The first architecture explores the impact of reducing the number of deep learning layers and the second splits the input images horizontally into two streams based on eye and mouth positions. The first proposed architecture produces state of the art results with an accuracy rate of 96.93 % and the second architecture with split input produces an average accuracy rate of 86.73 %, respectively.

Keywords

Deep learning Convolution neural networks Emotion recognition Empathic robots 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ariel Ruiz-Garcia
    • 1
    Email author
  • Mark Elshaw
    • 1
  • Abdulrahman Altahhan
    • 1
  • Vasile Palade
    • 1
  1. 1.Faculty of Engineering, Environment and Computing, School of Computing, Electronics and MathematicsCoventry UniversityCoventryUK

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