In this work, we explore the effectiveness of multimodal models for estimating the emotional state expressed continuously in the Valence/Arousal space. We consider four modalities typically adopted for the emotion recognition, namely audio (voice), video (face expression), electrocardiogram (ECG), and electrodermal activity (EDA), investigating different mixtures of them. To this aim, a CNN-based feature extraction module is adopted for each of the considered modalities, and an RNN-based module for modelling the dynamics of the affective behaviour. The fusion is performed in three different ways: at feature-level (after the CNN feature extraction), at model-level (combining the RNN layer’s outputs) and at prediction-level (late fusion). Results obtained on the publicly available RECOLA dataset, demonstrate that the use of multiple modalities improves the prediction performance. The best results are achieved exploiting the contribution of all the considered modalities, and employing the late fusion, but even mixtures of two modalities (especially audio and video) bring significant benefits.

Exploring Fusion Strategies in Deep Multimodal Affect Prediction / S. Patania, A. D'Amelio, R. Lanzarotti (LECTURE NOTES IN COMPUTER SCIENCE). - In: Image Analysis and Processing – ICIAP 2021 / [a cura di] S. Sclaroff, C. Distante, M. Leo, G.M. Farinella, F. Tombari. - [s.l] : Springer Verlag, 2022. - ISBN 978-3-031-06429-6. - pp. 730-741 (( convegno International Conference on Image Analysis and Processing, ICIAP 2021 tenutosi a Lecce nel 2022 [10.1007/978-3-031-06430-2_61].

Exploring Fusion Strategies in Deep Multimodal Affect Prediction

S. Patania
;
A. D'Amelio;R. Lanzarotti
2022

Abstract

In this work, we explore the effectiveness of multimodal models for estimating the emotional state expressed continuously in the Valence/Arousal space. We consider four modalities typically adopted for the emotion recognition, namely audio (voice), video (face expression), electrocardiogram (ECG), and electrodermal activity (EDA), investigating different mixtures of them. To this aim, a CNN-based feature extraction module is adopted for each of the considered modalities, and an RNN-based module for modelling the dynamics of the affective behaviour. The fusion is performed in three different ways: at feature-level (after the CNN feature extraction), at model-level (combining the RNN layer’s outputs) and at prediction-level (late fusion). Results obtained on the publicly available RECOLA dataset, demonstrate that the use of multiple modalities improves the prediction performance. The best results are achieved exploiting the contribution of all the considered modalities, and employing the late fusion, but even mixtures of two modalities (especially audio and video) bring significant benefits.
Multimodal emotion recognition; Deep learning; Multimodal fusion
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/930966
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