Unified framework for in-the-wild recognition of basic and compound facial expressions using label distribution learning and dynamic ensemble selection

Key Points

• Recognition of basic and compound facial expressions is enhanced significantly with label distribution learning.
• An accuracy of 85% was achieved on varied emotional datasets across different conditions.
• The approach combines machine learning techniques with dynamic ensemble selection for adaptability.
• This method highlights the potential to improve emotion recognition systems in real-world applications.