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End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation

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2020

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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.

Beschreibung

Jalilian, Ehsaneddin; Karakaya, Mahmut; Uhl, Andreas (2020): End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation. BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-700-5. pp. 117-128. Regular Research Papers. International Digital Conference. 16.-18. September 2020

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