Towards Emotion Recognition: A Persistent Entropy Application
This work addresses emotion recognition for applications like human-computer interaction, but it is incremental as it combines existing methods (persistent entropy and SVMs) on a new dataset.
The paper tackled emotion classification by applying persistent entropy to extract features from raw signals and using support vector machines to classify them into eight emotions, achieving results in a new application area.
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).