MULTIMODAL EMOTION RECOGNITION USING DEEP LEARNING NETWORKS

This book Multimodal Emotion Recognition Using Deep Learning Networks focuses on improving emotion recognition by combining multiple data sources (modalities) like facial expressions EEG and Physiological signals. Deep learning models are used to extract features from each modality and fusion techniques (such as late fusion approach) integrate these features to make more accurate emotion predictions. The study shows that multimodal fusion significantly boosts performance over single-modality systems highlighting the importance of combining complementary emotional cues using advanced neural network architectures.