Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment
Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment
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DOI：
10.3390/sym9110263
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发表时间：
2017-11-01
期刊：
影响因子：
2.7
通讯作者：
Park, Kang Ryoung
中科院分区：
文献类型：
--
作者：
Arsalan, Muhammad;Hong, Hyung Gil;Park, Kang Ryoung
现有的虹膜识别系统严重依赖于特定条件，例如图像获取的距离和停下来凝视的环境，这些都需要大量的用户合作。在用户合作不能得到保证的环境中，虹膜区域的主流分割方案面临着许多问题，例如睫毛的严重遮挡、无效的离轴旋转、运动模糊以及眼睛区域的非规则反射。此外，为了避免使用额外的近红外（NIR）摄像机和NIR照明器，由于可见光环境噪声的影响，增加了虹膜区域准确分割的难度，研究了基于可见光环境的虹膜识别方法。解决这些问题;摘要提出了一种基于卷积神经网络（CNN）的两阶段虹膜分割方法，该方法能够在可见光摄像传感器虹膜识别的严重噪声环境中实现准确的虹膜分割。在实验中;使用有噪虹膜挑战评估第II部分（NICE-II）训练数据库（从UBIRIS.v2数据库中选择）和移动的虹膜挑战评估（MICHE）数据集。实验结果表明，该方法优于现有的分割方法。
Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.