An emotional coherence analysis with a multimodal Neural Network. A study based on a novel affective database of facial expression
Fabio Tatti
An emotional coherence analysis with a multimodal Neural Network. A study based on a novel affective database of facial expression.
Rel. Federica Marcolin, Luca Ulrich, Alberto Raposo, Daniel Mograbi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023
Abstract
A new technology called Facial Emotion Recognition (FER) tries to replicate exactly the decoding phase of the emotion from the facial expression by exploiting the state of the art AI techniques. The technique needs, in order to be performed, to leverage on a dataset of facial expression previously annotated in such a way to have a ground truth. The dataset itself was built in a collaboration between the Politecnico di Torino and the Pontifıcia Universidade Catolica do Rio de Janeiro, in order to create a dataset of Brazilian faces. The data were collected by showing to volunteers 48 emotional images, in random order, selected from the IAPS and GAPED databases.
Meanwhile a RGB + Depth sensor was filming their faces to capture the emotion
Tipo di pubblicazione
URI
 |
Modifica (riservato agli operatori) |
