Expression Recognition based on Emotion-Wheel-Guided Label Distribution Learning

Facial expression recognition (FER) is a crit-ical component in many fields, such as human-computer interaction and affective computing. However, existing FER methods face several key challenges such as label ambiguity, mixed emotional expressions, and class imbalance. To ad-dress these issues, we propose a novel framework based on Label Distribution Learning (LDL) that captures the complex and compound nature of real-world emotions. Our approach introduces MixFeature, a feature-level augmentation strategy that synthesizes new samples with label distribution by mix-ing single-label ones. Such process is guided by the Robert Plutchik’s Emotion Wheel to ensure semantic consistency in the generated label distribution. By modeling emotions as label distribution, our method provides a more nuanced repre-sentation of blended emotional states. Extensive experiments on widely used datasets demonstrate that our framework sig-nificantly outperforms existing single-label and LDL methods in recognition accuracy and robustness, particularly in han-dling ambiguous and mixed emotions and addressing class imbalance.