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Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks
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Text/Conference Paper
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Datum
2018
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Verlag
Köllen Druck+Verlag GmbH
Zusammenfassung
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 non-iris 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.