Multi-spectral Iris Segmentation in Visible Wavelengths

Abstract

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 a vital step in iris recognition. The segmentation of the iris usually involves a detection of inner and outer iris boundaries, a detection of eyelids, an exclusion of eyelashes as well as contact lens rings and a scrubbing of specular reflections. This work presents a comprehensive multi-spectral analysis to improve iris segmentation accuracy in visible wavelengths by transforming iris images before their segmentation, which is done by extracting spectral components in form of RGB color channels. The procedure is evaluated by utilizing the MobBIO dataset, open-source iris segmentation tools, and the NICE.I error measures. Additionally, a segmentation-level fusion procedure based on existing work is performed; an eye color analysis is examined, with no clear connection to the multi-spectral procedure being found; and another analysis highlights further potential improvement by assuming perfect selection within various multi-spectral segmentation result sets.