CE-KD: Class-Wise Expert-Based Knowledge Distillation for Facial Expression Recognition

Knowledge distillation (KD) is a model compression technique that transfers knowledge from a complex and well-trained teacher model to a compact student model, thereby enabling the student to mimic the performance and behavior of the teacher. However, traditional KD methods struggle with long-tailed facial expression recognition (FER), as FER datasets often exhibit severe class imbalance. For instance, certain expressions (e.g., happiness) are overrepresented, while others (e.g., fear) have significantly fewer samples. This innate class-imbalanced property of FER leads to suboptimal knowledge transfer for the underrepresented expressions (i.e. biased learning and poor generalization for underrepresented classes). To address this issue, this paper introduces the CE-KD, a Class-wise Expert-based Knowledge Distillation framework that enables the student model to effectively learn fine-grained expression details and high-level emotional concepts from specialized emotion experts. This improves the generalizability of the FER models. Extensive experiments on benchmark datasets — FERPlus and RAF-DB — demonstrate that our CE-KD framework provides a practical solution to implement efficient FER systems in real-world applications while maintaining robust performance across different emotion expressions.