FaceMoE: Mixture of Experts for Low-Resolution Face Recognition

Low-resolution face recognition (LR-FR) remains a challeng-ing task due to poor feature extraction and aggregation, as probe imagesoften contain limited identity information resulting from extreme degrada-tions such as blur, occlusion, and low contrast. Additionally, the domaingap between high-resolution (HR) gallery images and low-resolution (LR)probe images poses a significant challenge. A single feature encoder strug-gles to generalize effectively across both domains when fine-tuned on anLR dataset, and this issue is further magnified by catastrophic forget-ting. To address these challenges, we propose FaceMoE, an effectiveadaptation of Mixture of Experts (MoE) transfomer architecture for low-resolution face-recognition . Specifically, we introduce multiple specializedfeed-forward network (FFN) experts and incorporate a top-k router, whichdynamically assigns tokens to appropriate experts. This design emergentlypromotes specialization across experts for different semantic regions ofthe face, which enables FaceMoE to perform resolution-aware featureextraction. Moreover, the top-k router facilitates sparse expert activation,enabling the model to preserve pretrained knowledge when finetuned on aLR dataset, while increasing model capacity without proportional compu-tational overhead. FaceMoE is trained with a combined face recognitionloss, router z-loss, and load balancing loss to ensure expert specializa-tion and stable training. To the best of our knowledge, this is the firstwork leveraging MoE for LR-FR. Extensive experiments across elevendatasets, spanning HR, mixed-quality, and LR benchmarks, demonstratethat FaceMoE significantly outperforms state-of-the-art methods.