Jalilian, EhsaneddinKarakaya, MahmutUhl, AndreasBrömme, ArslanBusch, ChristophDantcheva, AntitzaRaja, KiranRathgeb, ChristianUhl, Andreas2020-09-162020-09-162020978-3-88579-700-5https://dl.gi.de/handle/20.500.12116/34319While 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.enOff-angle iris segmentationOff-angle iris recognitionIris parameterizationConvolutional neural networkCNNText/Conference Paper1617-5468