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

arXiv: https://arxiv.org/abs/1912.04711 · HTML: https://mlsanity.com/paper/1912.04711/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Deep adaptative spectral zoom for improved remote heart rate estimation](https://mlsanity.com/paper/2403.06902/) (2024)
- [An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild](https://mlsanity.com/paper/2102.09150/) (2021)
- [Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset](https://mlsanity.com/paper/2104.12345/) (2021)
- [CAST-Phys: Contactless Affective States Through Physiological signals Database](https://mlsanity.com/paper/2507.06080/) (2025)

## Abstract (arXiv)

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.