MVP: Multimodal Emotion Recognition based on Video and Physiological Signals - EURECOM
Communication Dans Un Congrès Année : 2024

MVP: Multimodal Emotion Recognition based on Video and Physiological Signals

Résumé

Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using classic machine learning, rather than recent deep learning techniques. We propose to fill this gap, designing the Multimodal for Video and Physio (MVP) architecture, streamlined to fuse video and physiological signals. Differently then others approaches, MVP exploits the benefits of attention to enable the use of long input sequences (1-2 minutes). We have studied video and physiological backbones for inputting long sequences and evaluated our method with respect to the state-of-the-art. Our results show that MVP outperforms former methods for emotion recognition based on facial videos, EDA, and ECG/PPG.
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hal-04869231 , version 1 (07-01-2025)

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Valeriya Strizhkova, Hadi Kachmar, Hava Chaptoukaev, Raphael Kalandadze, Natia Kukhilava, et al.. MVP: Multimodal Emotion Recognition based on Video and Physiological Signals. ABAW 2024 - 7th Workshop and Competition on Affective Behavior Analysis in-the-Wild, Sep 2024, Milan, Italy. ⟨hal-04869231⟩
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