DDFR: Degradation-Disentangled Diffusion-Regularized Feature Correction for Blurred-Query Face Retrieval

Face retrieval across varying degrees of Gaussian blur is challenging because clean-gallery and blurred-query representations are inherently asymmetric: conventional systems assume sufficiently clear probe and gallery images, whereas real deployments often match blurred probes against clean stored identities. Gaussian blur suppresses discriminative cues and displaces the blurred-query embedding from its clean-gallery identity anchor, degrading cosine ranking. Existing recognizers compare degraded queries directly with clean-gallery embeddings, while blind face restoration (BFR) preprocessing pipelines optimize visual quality rather than retrieval alignment. We introduce DDFR, a degradation-aware and diffusion-regularized framework that corrects blur-induced displacement in feature space. A shared encoder produces identity and degradation representations; the latter estimates an identity-space correction vector and is regularized by diffusion-style latent denoising. Directional separation discourages identity-aligned correction, and the estimated vector is subtracted from the blurred-query representation before retrieval. We further construct a dedicated identity-disjoint Gaussian-blur face retrieval dataset covering σ = 1, ..., 10. On this benchmark, DDFR achieves 88.05 ± 1.26% average Rank-1 accuracy, while ArcFace and AdaFace trained on the same constructed dataset achieve 59.52% and 65.58%, respectively. At σ = 10, DDFR retains 60.76 ± 3.74% Rank-1 accuracy, compared with 21.37% and 28.06% for the same references. Component analyses support degradation modeling, latent denoising, and directional separation; the extended sweep identifies the limit imposed by severe information loss. These results suggest a path for lower-resolution camera inputs to approach the retrieval utility of clearer, higher-quality captures when identity cues remain recoverable.