Facial Affective Behavior Analysis

Facial affective behavior analysis (FABA) aims to understand human emotions and mental states by analyzing facial expressions and movements. Current research heavily focuses on developing robust and efficient deep learning models, including masked autoencoders and multi-modal large language models, often incorporating techniques like contrastive learning and identity adversarial training to improve accuracy and fairness across diverse datasets. These advancements are driving progress in applications such as human-computer interaction, mental health assessment, and improving the understanding of social dynamics through the development of open-source toolkits and benchmarks.