ThirdEye: Triplet Based Iris Recognition without Normalization
ThirdEye: Triplet Based Iris Recognition without Normalization
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DOI：
10.1109/btas46853.2019.9185998
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发表时间：
2019-07
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影响因子：
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通讯作者：
Sohaib Ahmad;Benjamin Fuller
中科院分区：
文献类型：
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作者：
Sohaib Ahmad;Benjamin Fuller
大多数虹膜识别流程包括三个阶段：分割成虹膜/非虹膜像素，将虹膜区域归一化为固定区域，并提取相关特征进行比较。鉴于深度学习的最新进展，谨慎地询问准确的虹膜识别需要哪些阶段。Lojez等人（IWBF 2019）最近得出结论，分割阶段对于良好的准确性仍然至关重要。我们问正常化是否有益？为了回答这个问题，我们开发了一种新的虹膜识别系统，称为基于三重卷积神经网络的ThirdEye（Schroff等人，ICCV 2015）。ThirdEye直接使用分割图像而不进行归一化。我们在ND-0405、UbirisV 2和IITD数据集上观察到的错误率分别为1.32%、9.20%和0.59%。对于IITD，最受约束的数据集，这改进了最好的先验工作。然而，对于ND-0405和UbirisV 2，我们的等错误率比以前的系统稍差。我们的结论假设是，规范化是更重要的约束较少的环境。
Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning, it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for good accuracy. We ask if normalization is beneficial?Towards answering this question, we develop a new iris recognition system called ThirdEye based on triplet convolutional neural networks (Schroff et al., ICCV 2015). ThirdEye directly uses segmented images without normalization.We observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. For IITD, the most constrained dataset, this improves on the best prior work. However, for ND-0405 and UbirisV2, our equal error rate is slightly worse than prior systems. Our concluding hypothesis is that normalization is more important for less constrained environments.