End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation
End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation
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发表时间：
2020-09
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通讯作者：
Ehsaneddin Jalilian;M. Karakaya;A. Uhl
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作者：
Ehsaneddin Jalilian;M. Karakaya;A. Uhl
虽然深度学习技术越来越多地成为虹膜分割的首选工具，但还没有专门用于使用此类模块进行偏角虹膜识别的综合识别框架。在这项工作中，我们研究了不同注视角度对基于CNN的偏角虹膜分割及其识别性能的影响，并引入了一种改进方案来补偿由偏角失真引起的一些分割退化。此外，我们提出了一个偏角参数化算法重新投影的偏角图像回正面视图。利用这些，我们进一步研究：（i）改善分割输出和/或在分割之前或之后校正虹膜图像，是否可以补偿偏角失真，或者（ii）通过在不同凝视角度的虹膜图像上训练网络，可以提高网络的泛化能力。在每一步实验中，对分割精度和识别性能进行了评估，并对结果进行了分析和比较。
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.