Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks

Iris segmentation under visible wavelengths (VWs) is a vital processing step for iris recognition systems operating at-a-distance or in non-cooperative environments. In these scenarios the presence of various artefacts, e.g. occlusions or specular reflections, as well as out-of-focus blur represents a significant challenge. The vast majority of proposed iris segmentation algorithms under VW aim at discriminating the iris and noniris regions without taking into account the variability that is present in the non-iris region. In this paper, we introduce the idea of segmenting the iris region using a multi-class approach which differentiates additional classes, e.g. pupil or sclera, as opposed to commonly employed bi-class approaches (iris and non-iris). Experimental results conducted on two publicly available databases show that the use of the proposed multi-class approach improves the iris segmentation accuracy. Simultaneously, it also allows for the segmentation of different non-iris regions, e.g. glasses, which could be employed in further application scenarios.