Child FER: Domain-Agnostic Facial Expression Recognition in Children Using a Secondary Image Diffusion Model

Facial expression recognition (FER) models often face challenges when generalizing across domains, such as different datasets and age groups. Despite the significance of this problem, FER in children (child FER) research remains relatively understudied, and such studies exhibit vulnerability to cross-domain evaluation. In response to the scarcity of child FER research, we propose a novel domain-agnostic approach for robust child FER. The architecture integrates a child-centric source-domain reconstructor and a child emotion feature-guided classifier. First, we use a secondary image diffusion model to reconstruct the image with source-domain childlike features while preserving emotion. Second, we recognize facial expressions based on domain-agnostic features from reconstructed images through a cross-attention mechanism. This approach counters performance degradation caused by domain discrepancies and improves the generalizability of child FER. We validate the proposed approach with diverse, publicly available datasets to highlight its effectiveness. The source code is available at https://github.com/st0421/Child-FER.