End-to-end facial and physiological model for Affective Computing and applications

TL;DR

This paper introduces a deep learning-based multi-modal emotion recognition model combining facial expressions and physiological signals, with applications in affective computing and medical therapy assessment.

Contribution

It presents a novel multi-modal deep learning model with latent feature extraction for emotion recognition and demonstrates its application in anxiety therapy assessment.

Findings

Effective emotion classification on AMIGOS dataset

Successful tracking of emotional changes during therapy

Improved accuracy with latent feature extraction

Abstract

In recent years, Affective Computing and its applications have become a fast-growing research topic. Furthermore, the rise of Deep Learning has introduced significant improvements in the emotion recognition system compared to classical methods. In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions. Moreover, we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder (BAE). Both models are trained and evaluated on AMIGOS datasets reporting valence, arousal, and emotion state classification. Finally, to demonstrate a possible medical application in affective computing using deep learning techniques, we applied the proposed method to the assessment of anxiety therapy. To this purpose, a reduced multi-modal database has been collected by recording facial expressions and peripheral signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR) of each patient. Valence and arousal estimation was extracted using the proposed model from the beginning until the end of the therapy, with successful evaluation to the different emotional changes in the temporal domain.