# Iris Image Processing in Compressive Sensing Scenario

## TL;DR

This paper explores the use of Compressive Sensing techniques for reconstructing under-sampled iris images to improve biometric identification, comparing different sparsity domains and validating the approach on real iris data.

## Contribution

It introduces a novel application of Compressive Sensing to iris image reconstruction, analyzing various sparsity domains and their effectiveness in biometric recognition.

## Key findings

- Compressive Sensing can effectively reconstruct iris images from limited data.
- Different sparsity domains impact reconstruction quality.
- The approach is validated on real iris images.

## Abstract

This paper observes the application of the Compressive Sensing in reconstruction of the under-sampled iris images. Iris recognition represents form of biometric identification whose usage in real applications is growing. Compressive Sensing represents a novel form of sparse signal acquisition and recovering when small amount of data is a available. Different sparsity domains are considered and compared using various number of available image pixels. The theory is verified on iris images.

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Source: https://tomesphere.com/paper/1902.03123