Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
This work addresses the challenge of enabling iris recognition under relaxed acquisition conditions, which is incremental as it builds on existing super-resolution and fusion techniques.
The paper tackled the problem of iris recognition from very low-resolution images by using Eigen-patch super-resolution and matcher fusion, achieving an equal error rate below 5% for images as small as 13x13 pixels.
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13x13.