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End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation
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Datum
2020
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
While deep learning techniques are increasingly becoming a tool of choice for iris segmentation,
yet there is no comprehensive recognition framework dedicated for off-angle iris recognition
using such modules. In this work, we investigate the effect of different gaze-angles on the CNN
based off-angle iris segmentations, and their recognition performance, introducing an improvement
scheme to compensate for some segmentation degradations caused by the off-angle distortions. Also,
we propose an off-angle parameterization algorithm to re-project the off-angle images back to frontal
view. Taking benefit of these, we further investigate if: (i) improving the segmentation outputs and/or
correcting the iris images before or after the segmentation, can compensate for off-angle distortions,
or (ii) the generalization capability of the network can be improved, by training it on iris images of
different gaze-angles. In each experimental step, segmentation accuracy and the recognition performance
are evaluated, and the results are analyzed and compared.