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Biometric Template Selection: A Case Study in Fingerprints

机译:生物识别模板选择:指纹案例研究

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摘要

A biometric authentication system operates by acquiring bio-metric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user to account for variations in a person's biometric data. In this paper we propose two techniques to automatically select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, performs clustering in order to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that have maximum similarity with the rest of the impressions and, therefore, represent typical measurements of biometric data. Matching results on a database of 50 different fingers, with 100 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection.
机译:生物特征认证系统通过从用户获取生物特征数据并将其与存储在数据库中的模板数据进行比较来进行操作,以便识别人员或验证要求的身份。大多数系统为每个用户存储多个模板,以解决一个人的生物特征数据的变化。在本文中,我们提出了两种技术,可以从一组给定的指纹印象中自动选择手指的原型指纹模板。第一种方法称为DEND,执行聚类以便选择最能代表类内差异的模板集,而第二种方法称为MDIST,选择与其余展示次数具有最大相似度的模板,从而代表生物特征数据的典型测量。在具有50个不同手指的数据库上的匹配结果(每个手指100次印象)表明,与随机模板选择相比,此处介绍的系统模板选择过程可提供更好的性能。

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