Affective Behavior Analysis

Affective behavior analysis focuses on developing technology capable of recognizing and responding to human emotions from visual and audio data, primarily through analyzing facial expressions, body language, and vocal cues. Current research heavily utilizes deep learning models, particularly convolutional neural networks (CNNs) and transformers, often employing multi-task learning and ensemble methods to improve accuracy in recognizing basic and compound emotions, valence-arousal levels, and action units. This field is crucial for advancing human-computer interaction, enabling the creation of more empathetic and responsive systems across various applications, including healthcare, education, and entertainment.