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Multibiometric Template Security Using Fuzzy Vault

机译:使用模糊拱顶的多学术模板安全

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Template security is a critical issue in biometric systems because biometric templates cannot be easily revoked and reissued. While multibiometric systems overcome limitations such as non-universality and high error rates that affect unibiometric systems, they require storage of multiple templates for the same user. Securing the different templates of a user separately is not optimal in terms of security. Hence, we propose a scheme for securing multiple templates of a user as a single entity. We derive a single multibiometric template from the individual templates and secure it using the fuzzy vault framework. We demonstrate that a multibiometric vault provides better recognition performance and higher security compared to a unibiometric vault. For example, our multibiometric vault based on fingerprint and iris achieves a GAR of 98.2% at a FAR of ?? 0.01%, while the corresponding GAR values of the individual iris and fingerprint vaults are 88% and 78.8%, respectively. Further, we also show that the security of the system is only 41 bits when the iris and fingerprint vaults are stored separately. On the other hand, the multibiometric vault based on fingerprint and iris provides 49 bits of security.
机译:模板安全性是生物识别系统中的一个关键问题,因为生物识别模板不能轻易撤销和重新发布。虽然多学会系统克服了影响Unibiometric系统的非普遍性和高误差率的限制,但它们需要为同一用户存储多个模板。在安全性方面,单独保护用户的不同模板是不是最佳的。因此,我们提出了一种用于将用户的多个模板固定为单个实体的方案。我们从各个模板中得出一个多学术模板,并使用模糊的Vault框架来保护它。我们展示了与Unibiometric Vault相比,多学徒库提供了更好的识别性能和更高的安全性。例如,我们基于指纹和虹膜的多学料拱顶在远远达到98.2%的大小0.01%,而单个虹膜和指纹拱顶的相应GAN值分别为88%和78.8%。此外,我们还表明,当单独存储虹膜和指纹保管库时,系统的安全性仅是41位。另一方面,基于指纹和虹膜的多学术库提供了49位的安全性。

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