A Unified Transformer-based Network for multimodal Emotion Recognition

• URL: http://arxiv.org/abs/2308.14160v1
• Date: Sun, 27 Aug 2023 17:30:56 GMT
• Title: A Unified Transformer-based Network for multimodal Emotion Recognition
• Authors: Kamran Ali and Charles E. Hughes
• Abstract summary: We present a transformer-based method to classify emotions in an arousal-valence space by combining a 2D representation of an ECG/ signal with the face information. Our model produces comparable results to the state-of-the-art techniques.
• Score: 4.07926531936425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of transformer-based models has resulted in significant advances in addressing various vision and NLP-based research challenges. However, the progress made in transformer-based methods has not been effectively applied to biosensing research. This paper presents a novel Unified Biosensor-Vision Multi-modal Transformer-based (UBVMT) method to classify emotions in an arousal-valence space by combining a 2D representation of an ECG/PPG signal with the face information. To achieve this goal, we first investigate and compare the unimodal emotion recognition performance of three image-based representations of the ECG/PPG signal. We then present our UBVMT network which is trained to perform emotion recognition by combining the 2D image-based representation of the ECG/PPG signal and the facial expression features. Our unified transformer model consists of homogeneous transformer blocks that take as an input the 2D representation of the ECG/PPG signal and the corresponding face frame for emotion representation learning with minimal modality-specific design. Our UBVMT model is trained by reconstructing masked patches of video frames and 2D images of ECG/PPG signals, and contrastive modeling to align face and ECG/PPG data. Extensive experiments on the MAHNOB-HCI and DEAP datasets show that our Unified UBVMT-based model produces comparable results to the state-of-the-art techniques.

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