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Towards Emotion Recognition: A Persistent Entropy Application

  • Rocio Gonzalez-Diaz
  • Eduardo Paluzo-Hidalgo
  • José F. Quesada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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).

Keywords

Persistent homology Persistent entropy Emotion recognition Support vector machine 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rocio Gonzalez-Diaz
    • 1
  • Eduardo Paluzo-Hidalgo
    • 1
  • José F. Quesada
    • 2
  1. 1.Department of Applied Mathematics IUniversity of SevilleSevilleSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of SevilleSevilleSpain

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