End-to-end facial and physiological model for Affective Computing and applications
This work addresses emotion recognition for affective computing applications, such as medical therapy assessment, but is incremental as it builds on existing deep learning and multi-modal approaches.
The authors tackled emotion recognition by proposing a multi-modal deep learning model combining facial expressions and physiological signals, reporting evaluation on the AMIGOS dataset for valence, arousal, and emotion classification, and applied it to assess anxiety therapy with successful temporal evaluation of emotional changes.
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.