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Deep ConvNet for Facial Emotion Recognition

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) (AI2SD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1105))

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Abstract

Facial expression recognition has made great progress over the last two decades. The growth in the use of deep learning has contributed significantly to this advance. In this work, we proposed a Deep ConvNet architecture for facial expression recognition based on the RaFD dataset. Results show that the best setup is the combination with different parameters like the convolution layer number, RELU function activation in hidden layers, Adam optimizer, 2 fully connected layers…etc. The best architecture gives over 97% accuracy which is promising compared to the state-of-the-art results and confirms the effectiveness and robustness of DCNN with batch-normalization.

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Author information

Authors and Affiliations

  1. Laboratoire d’automatique de Tlemcen (LAT), Tlemcen University, Tlemcen, Algeria

    Wahida Handouzi & Ahmed Ziane

Authors
  1. Wahida Handouzi
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  2. Ahmed Ziane
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Corresponding author

Correspondence to Wahida Handouzi .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Handouzi, W., Ziane, A. (2020). Deep ConvNet for Facial Emotion Recognition. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-030-36674-2_22

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