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首页> 外文期刊>Journal of visual communication & image representation >Attention guided U-Net for accurate iris segmentation
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Attention guided U-Net for accurate iris segmentation

机译：注意引导的U-Net可以准确分割虹膜

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摘要

Iris segmentation is a critical step for improving the accuracy of iris recognition, as well as for medical concerns. Existing methods generally use whole eye images as input for network learning, which do not consider the geometric constrain that iris only occur in a specific area in the eye. As a result, such methods can be easily affected by irrelevant noisy pixels outside iris region. In order to address this problem, we propose the ATTention U-Net (Arf-UNet) which guides the model to learn more discriminative features for separating the iris and non-iris pixels. The ATT-UNet firstly regress a bounding box of the potential iris region and generated an attention mask. Then, the mask is used as a weighted function to merge with discriminative feature maps in the model, making segmentation model pay more attention to iris region. We implement our approach on UBIRIS.v2 and CASIA.IrisV4-distance, and achieve mean error rates of 0.76% and 0.38%, respectively. Experimental results show that our method achieves consistent improvement in both visible wavelength and near-infrared iris images with challenging scenery, and surpass other representative iris segmentation approaches. (C) 2018 Elsevier Inc. All rights reserved.
机译：虹膜分割是提高虹膜识别精度以及医疗问题的关键步骤。现有方法通常将全眼图像用作网络学习的输入，它们没有考虑虹膜仅出现在眼睛的特定区域的几何约束。结果，这种方法容易受到虹膜区域之外的无关的噪声像素的影响。为了解决此问题，我们提出了ATTention U-Net（Arf-UNet），该模型指导模型学习更多区分虹膜和非虹膜像素的判别特征。 ATT-UNet首先使潜在虹膜区域的边界框回归并生成注意蒙版。然后，将遮罩作为加权函数与模型中的判别特征图合并，从而使分割模型更加关注虹膜区域。我们在UBIRIS.v2和CASIA.IrisV4-distance上实现了我们的方法，并实现了0.76％和0.38％的平均错误率。实验结果表明，我们的方法在可见光和近红外虹膜图像中均获得了持续改进，并且具有极具挑战性的风景，并且超过了其他代表性虹膜分割方法。 （C）2018 Elsevier Inc.保留所有权利。

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