Affect Recognition

Affect recognition, the computational identification of human emotions and affective states, aims to build systems capable of understanding and responding to emotional cues. Current research heavily utilizes machine learning, particularly deep learning models like transformers and residual networks, often incorporating multimodal data (e.g., facial expressions, speech, physiological signals) and leveraging techniques like transfer learning and semi-supervised learning to improve accuracy and address data limitations. This field is significant for advancing human-computer interaction, personalized learning environments, and mental health applications, with ongoing efforts focused on improving model robustness and generalizability across diverse contexts and populations.