| Título |
Towards Emotion Recognition: A Persistent Entropy Application |
| Autores |
Gonzalez-Diaz, Rocio , PALUZO HIDALGO, EDUARDO, Quesada, Jose F. |
| Publicación externa |
Si |
| Medio |
Lecture Notes in Computer Science |
| Alcance |
Proceedings Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.427 |
| Fecha de publicacion |
01/01/2019 |
| ISI |
000684199500008 |
| 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). |
| Palabras clave |
Persistent homology; Persistent entropy; Emotion recognition; Support vector machine |
| Miembros de la Universidad Loyola |
|
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