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International Symposium on Modelling and Implementation of Complex Systems

MISC 2020: Modelling and Implementation of Complex Systems pp 188–202Cite as

A Simple Yet Effective Convolutional Neural Network Model to Classify Facial Expressions

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 156))

Abstract

Facial Expression Recognition (FER) is a form of nonverbal communication; it translates the internal and emotional state of a human being by changing one or several facial muscles. Automated classification of facial expressions has known a great progress over the last decade; we observed the appearance of new methods based on Deep Learning (DL) instead of traditional classification methods. In this paper we propose an improved method based on Convolutional Neural Networks (CNN) that responds to the problem of classification of the six basic emotions(anger, disgust, fear, happy, sad and surprise) plus the neutral case. We validated our model on three public databases and we achieved better results than the state-of-the-art: CK+ 88,23%, JAFFE 86.24%, KDEF 82.38%. Our accuracies out perform results from recently proposed traditional methods as well as DL based methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ferhat Abbas Setif1, Setif, Algeria

    Meriem Sari & Abdelouahab Moussaoui

  2. Center for Machine Learning and Signal Analysis, University of Oulu, Oulu, Finland

    Abdenour Hadid

Authors
  1. Meriem Sari
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  2. Abdelouahab Moussaoui
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  3. Abdenour Hadid
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Corresponding author

Correspondence to Meriem Sari .

Editor information

Editors and Affiliations

  1. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Salim Chikhi

  2. Faculty of Technology, University of Saida, Saida, Algeria

    Abdelmalek Amine

  3. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Allaoua Chaoui

  4. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Djamel Eddine Saidouni

  5. Faculty of Science and Technology, Department of Mathematics and Computer Science, University of El Oued, El-Oued, Algeria

    Mohamed Khireddine Kholladi

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Sari, M., Moussaoui, A., Hadid, A. (2021). A Simple Yet Effective Convolutional Neural Network Model to Classify Facial Expressions. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D., Kholladi, M. (eds) Modelling and Implementation of Complex Systems. MISC 2020. Lecture Notes in Networks and Systems, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-58861-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-58861-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58860-1

  • Online ISBN: 978-3-030-58861-8

  • eBook Packages: EngineeringEngineering (R0)

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