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  1. Jan 7, 2025Human 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 ...
  2. Jan 6, 2025While unimodal video and physiological signals have been used to recognize emotions, very few works combine the two kind of data [24, 18, 5].A recent work from 2023, proposes to fuse traditional features, classifying them by SVM and k-NN [].Similarly, in a recent dataset for stress identification [], the baselines for both unimodal and multimodal experiments are conducted based on SVM/MLP ...
  3. www-sop.inria.fr

    improve the physiological backbone permitting the input of the full sequences, thus enabling attention to find correlation in both short and long range de-pendencies. Finally, we propose a multimodal transformer-based architecture, the Multimodal for Video and Physio (MVP), to efficiently couple the behav-
  4. semanticscholar.org

    The Multimodal for Video and Physio (MVP) architecture is designed, streamlined to fuse video and physiological signals, and shows that MVP outperforms former methods for emotion recognition based on facial videos, EDA, and ECG/PPG. . Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological ...
  5. 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).
  6. openaccess.thecvf.com

    Video-Based Multimodal Spontaneous Emotion Recognition Using Facial Expressions and Physiological Signals Yassine Ouzar, Frédéric Bousefsaf, Djamaleddine Djeldjli, Choubeila Maaoui ; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 2460-2469
  7. scanavan.github.io

    music videos to elicit emotions. 32 EEG channels based on the 10-20 system [16] for recording EEG data and 8 channels for peripheral physiological data were used. These signals were recorded with a sampling rate of 512 Hz which was downsampled to 128 Hz after preprocessing. The data was labeled with arousal, valence, dominance, and liking values
  8. Jun 5, 2023This paper aims to demonstrate the importance and feasibility of fusing multimodal information for emotion recognition. It introduces a multimodal framework for emotion understanding by fusing the information from visual facial features and rPPG signals extracted from the input videos. An interpretability technique based on permutation feature importance analysis has also been implemented to ...
  9. catalyzex.com

    Jan 6, 2025MVP: Multimodal Emotion Recognition based on Video and Physiological Signals: Paper and Code. 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 ...
  10. sciencedirect.com

    Jan 1, 2025Emotion is a complex state that integrates feelings, thoughts, and behaviors. It is a conscious or unconscious psychological and physiological process stimulated by internal or external stimuli [1].Compared with facial expressions, speech, or behavior, physiological signals are objective, reliable and uncontrollable, and can truly reflect the physiological changes caused by internal emotions ...

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