Facial Action Unit Recognition

Facial Action Unit (AU) recognition aims to automatically identify and interpret the individual muscle movements that constitute facial expressions, as defined by the Facial Action Coding System (FACS). Current research focuses on improving accuracy and robustness through advanced model architectures like Vision Transformers and Siamese networks, often incorporating techniques such as multi-scale analysis, attention mechanisms, and relationship modeling between different AUs to capture the complex interplay of facial movements. These advancements are driven by the need for more accurate and explainable AU recognition systems, with applications ranging from improved human-computer interaction to advancements in mental health assessment and lie detection. The field is also actively exploring weakly supervised and transfer learning methods to address the challenges posed by limited annotated data.