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International Workshop on Computational Topology in Image Context

CTIC 2019: Computational Topology in Image Context pp 96–109Cite as

Towards Emotion Recognition: A Persistent Entropy Application

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11382))

Abstract

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust and surprised).

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Notes

  1. 1.

    http://kristina-project.eu/en/.

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Acknowledgments

This research has been partially supported by MINECO, FEDER/UE under grant MTM2015-67072-P. We thank the anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics I, University of Seville, Seville, Spain

    Rocio Gonzalez-Diaz & Eduardo Paluzo-Hidalgo

  2. Department of Computer Science and Artificial Intelligence, University of Seville, Seville, Spain

    José F. Quesada

Authors
  1. Rocio Gonzalez-Diaz
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  2. Eduardo Paluzo-Hidalgo
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  3. José F. Quesada
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Corresponding author

Correspondence to Eduardo Paluzo-Hidalgo .

Editor information

Editors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Rebeca Marfil

  2. Universidad de Málaga, Málaga, Spain

    Mariletty Calderón

  3. University of Seville, Sevilla, Spain

    Fernando Díaz del Río

  4. University of Seville, Sevilla, Spain

    Pedro Real

  5. Universidad de Málaga, Málaga, Spain

    Antonio Bandera

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Gonzalez-Diaz, R., Paluzo-Hidalgo, E., Quesada, J.F. (2019). Towards Emotion Recognition: A Persistent Entropy Application. In: Marfil, R., Calderón, M., Díaz del Río, F., Real, P., Bandera, A. (eds) Computational Topology in Image Context. CTIC 2019. Lecture Notes in Computer Science(), vol 11382. Springer, Cham. https://doi.org/10.1007/978-3-030-10828-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-10828-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10827-4

  • Online ISBN: 978-3-030-10828-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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