摘要翻译：
我们提出了一种Ising模型的变体，称为种子Ising模型，以模拟人虹膜模板的概率性质。该模型是一个Ising模型，在Ising模型的演化过程中，某些晶格点的值是固定的。利用这一方法，我们给出了如何从部分信息重建完整的虹膜模板，并证明了在所得到的汉明距离在正确断言主体身份的范围内的情况下，大约需要给定模板的1/6才能恢复原始模板的几乎所有信息内容。由此我们提出了虹膜模板的有效统计自由度的概念，并给出了虹膜模板有效统计自由度约为总比特数的1/6。特别地，对于一个$2048$bits的模板，其有效统计自由度约为$342$bits，这与Daugman提出的完全不同的方法计算的自由度非常吻合。
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英文标题：
《Seeded Ising Model and Statistical Natures of Human Iris Templates》
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作者：
Song-Hwa Kwon and Hyeong In Choi and Sung Jin Lee and Nam-Sook Wee
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  We propose a variant of Ising model, called the Seeded Ising Model, to model probabilistic nature of human iris templates. This model is an Ising model in which the values at certain lattice points are held fixed throughout Ising model evolution. Using this we show how to reconstruct the full iris template from partial information, and we show that about 1/6 of the given template is needed to recover almost all information content of the original one in the sense that the resulting Hamming distance is well within the range to assert correctly the identity of the subject. This leads us to propose the concept of effective statistical degree of freedom of iris templates and show it is about 1/6 of the total number of bits. In particular, for a template of $2048$ bits, its effective statistical degree of freedom is about $342$ bits, which coincides very well with the degree of freedom computed by the completely different method proposed by Daugman.
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PDF链接：
https://arxiv.org/pdf/1802.02223