2025/01/06 by Valeriya Strizhkova, Hadi Kachmar, Strizhkova, Valeriya +24 · 1 voice
Computer Science · Neuroscience · Psychology · #68T05 #68T10 #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #I.5 #cs.CV
paper · pdf · doi:10.48550/arxiv.2501.03103
openalex publication_date 2025/01/06 · arxiv published 2025/01/06 · arxiv updated 2025/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.