# Towards Emotion Recognition: A Persistent Entropy Application

arXiv: https://arxiv.org/abs/1811.09607 · HTML: https://mlsanity.com/paper/1811.09607/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Topological Data Analysis for Speech Processing](https://mlsanity.com/paper/2211.17223/) (2022)
- [Topological Data Analysis Applications in Natural Language Processing: A Survey](https://mlsanity.com/paper/2411.10298/) (2024)
- [Emotion recognition in talking-face videos using persistent entropy and neural networks](https://mlsanity.com/paper/2110.13571/) (2021)

## Abstract (arXiv)

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).