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 (calm, happy, sad, angry, fearful, disgust and surprised).
@article{arxiv.1811.09607,
title = {Towards Emotion Recognition: A Persistent Entropy Application},
author = {R. Gonzalez-Diaz and E. Paluzo-Hidalgo and J. F. Quesada},
journal= {arXiv preprint arXiv:1811.09607},
year = {2019}
}