Human Emotion

Human emotion research aims to understand and model the complex interplay of physiological, psychological, and behavioral factors contributing to emotional experience. Current research focuses on developing robust multimodal emotion recognition systems using machine learning models like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often incorporating techniques like feature selection, transfer learning, and ensemble methods to improve accuracy and efficiency across various modalities (facial expressions, speech, physiological signals). This work has significant implications for improving human-computer interaction, mental health applications, and disaster response by enabling more accurate and nuanced understanding of emotional states in diverse contexts.