While traditional iris recognition systems operate using near-infrared images, visible wavelength approaches have gained attention in recent years due to a variety of reasons, such as the deployment of iris recognition in consumer grade mobile devices. Iris segmentation, the process of localizing the iris part of an image, is an important step in iris recognition systems. This work studies a simple but highly effective procedure to improve segmentation accuracy in visible wavelengths by transforming iris images before their segmentation, which is done by extracting multi-spectral components in form of RGB color channels. The procedure is evaluated by utilizing the MobBIO dataset, segmentation tools from USIT, and the NICE.I error measures. Additionally, a segmentation-level fusion procedure based on existing work is tested with negative results; an eye color analysis is examined, with no clear connection to the multi-spectral procedure being found; and another analysis highlights further potential for improvement by assuming perfect selection within various multi-spectral mask sets.
Torsten Schlett defended his Bachelor thesis on 'Multi-spectral Iris Segmentation in visible Wavelengths'