Towards Robust Iris Recognition Through Occlusion Identification and Conditional Diffusion-Based Reconstruction
2026-07-23 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on improving iris recognition when parts of the iris are blocked by things like eyelids or reflections. They created a system that first figures out what kind of blockage is present, then uses a special model to fill in the missing parts of the iris image. Finally, a neural network extracts detailed features from the restored image to identify the person. Tests showed this method works better than just using the visible parts alone.
iris recognitionbiometricsocclusionconvolutional neural network (CNN)diffusion modelimage reconstructionfeature extractionVGG19horizontal pyramid mappingCASIA-Iris-Thousand dataset
Authors
Kamrul Hasan, Mylene C. Q. Farias, Oleg V. Komogortsev
Abstract
Iris recognition is a reliable biometric approach that identifies individuals using the distinctive and stable texture of the iris. However, recognition performance can degrade when discriminative iris texture is partially occluded by eyelids, eyelashes, specular reflections, or other acquisition artifacts. Existing approaches often perform recognition directly on degraded samples or rely only on the remaining visible iris region, which may be inadequate when substantial texture is corrupted. To address this limitation, we propose an occlusion-aware iris recognition framework with three sequential modules: occlusion-type identification, diffusion-based reconstruction, and deep-learning-based recognition. First, a residual 2D CNN-based network determines whether an iris image is non-occluded or belongs to one of the controlled occlusion categories. Second, the occluded image, binary mask, and predicted occlusion type condition a denoising diffusion probabilistic model to reconstruct the corrupted region. Finally, VGG19-HPMNet, a modified VGG19 model with horizontal pyramid mapping, extracts discriminative global and part-wise local iris features for recognition. Experiments on the CASIA-Iris-Thousand dataset under a controlled synthetic-occlusion protocol show that the proposed framework improves iris recognition performance by identifying the occlusion type, reconstructing masked regions, and re-evaluating the restored iris samples.