Towards Robust Iris Recognition Through Occlusion Identification and Conditional Diffusion-Based Reconstruction

TL;DR - This paper proposes an occlusion-aware iris recognition pipeline that classifies occlusions, reconstructs corrupted regions with a conditional diffusion model, and extracts global and local features for identification. It aims to improve recognition when eyelids, eyelashes, reflections, or artifacts obscure substantial iris texture.

• A residual 2D CNN identifies non-occluded images and controlled occlusion types.
• A denoising diffusion model reconstructs masked regions using the image, binary mask, and predicted occlusion type as conditions.
• A modified VGG19 with horizontal pyramid mapping extracts global and part-wise iris features.
• Experiments use CASIA-Iris-Thousand with controlled synthetic occlusions and report improved recognition after reconstruction.