Iris Image Processing in Compressive Sensing Scenario
This work addresses iris recognition for biometric identification, but it appears incremental as it applies an existing method to a new domain without claiming major breakthroughs.
The paper tackles the problem of reconstructing under-sampled iris images using Compressive Sensing, comparing different sparsity domains and verifying the theory on iris images with various numbers of available pixels.
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.