摘要翻译：
虹膜识别技术通过拍摄眼睛的虹膜来识别个人，由于其易用性、准确性和在控制进入高度安全区域时的安全性，已经成为安全应用中的热门技术。融合多种算法来提高生物特征识别的性能受到了广泛的关注。该方法将过零的一维小波欧拉数和基于遗传算法的特征提取相结合。对这三种算法的输出进行归一化，并融合它们的得分来判断用户是真的还是假的。本文讨论了这种新的策略，以计算多模态组合得分。
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英文标题：
《Improving Iris Recognition Accuracy By Score Based Fusion Method》
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作者：
Ujwalla Gawande, Mukesh Zaveri, Avichal Kapur
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas. Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. The proposed method combines the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for feature extraction. The output from these three algorithms is normalized and their score are fused to decide whether the user is genuine or imposter. This new strategies is discussed in this paper, in order to compute a multimodal combined score.
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PDF链接：
https://arxiv.org/pdf/1007.0412