Fusion of Modalities for Emotion Recognition with Deep Learning

Abstract

The paper delineates a pioneering advancement in emotion recognition technology, showcasing a sophisticated multimodal system adept at discerning human emotions through the fusion of video, audio, and facial features, facilitated by state-of-the-art deep learning methodologies. By amalgamating information from diverse sensory modalities, the system attains remarkable precision in classifying emotions, surpassing the efficacy of unimodal approaches. Through a comprehensive array of experiments and evaluations, the study substantiates the system’s prowess in accurately deciphering emotional states. Notably, the fusion of multimodal cues enables nuanced insights into human affective responses, transcending the limitations of individual modalities and furnishing a holistic understanding of emotional dynamics.