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Towards Emotion Recognition: A Persistent Entropy Application

Authors

Gonzalez-Diaz, Rocio , PALUZO HIDALGO, EDUARDO, Quesada, Jose F.

External publication

Si

Means

Lect. Notes Comput. Sci.

Scope

Proceedings Paper

Nature

Científica

JCR Quartile

✗

SJR Quartile

✗

SJR Impact

0.427

Web

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061102220&doi=10.1007%2f978-3-030-10828-1_8&partnerID=40&md5=77eeb77f45b0a53ed1e0662955b4ef8a

Publication date

01/01/2019

ISI

000684199500008

Scopus Id

2-s2.0-85061102220

DOI

10.1007/978-3-030-10828-1_8

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