一种基于模糊提取的虹膜鉴别方案

模糊提取从生物特征输入中以容错的方式可靠地提取出均匀分布的随机密钥，当输入发生变化且变化很小时，该密钥可以保持不变.研究了当汉明距离作为生物特征匹配的度量标准时，结合纠错编码学与传统密码学实现模糊提取的虹膜鉴别方法，分析了虹膜特征编码之间的差异对正确鉴别性能的影响，设计了重复码和Reed-Solomon码的两层级联纠错编码方案，并对128只虹膜的各3个样本进行了模拟实验.所给方案使用户虹膜特征模板的安全性和隐私性得到了有效保护，并能够支持用户虹膜的注册更新.此外，实验表明该方案同时具有较理想的识别性能.

An Iris Authentication Scheme Based on Fuzzy Extractor

Fuzzy extractors allow one to extract some uniformly distributed random key in an error-tolerant way from a biometric input w and then successfully reproduce the key from any other biometric input w′ that is close to w. In this paper, a fuzzy extractor based iris authentication scheme is provided, which combines error correcting codes and cryptography, when Hamming distance is adopted as the biometric matching metric. The impacts of iris intra-class differences upon false rejection rate are analyzed and a two-layer coding scheme in which iterative codes and Reed-Solomon codes are applied is presented. In place of introducing mask filters to reduce iris code bit error rates, error correction capability is enhanced to strive for more iris valid entropy, when quality of iris image is not very good because of disturbance of eyelashes, eyelids, etc. The scheme is evaluated using 4096-bit iris codes from 128 different eyes, with 3 samples from each eye. Simulation experiments show that the false rejection rate is less than one percent. Additionally, both security and privacy of user’s biometric template can be well protected, and user registration update can also be supported. The proposed scheme is especially applicable to iris authentication system with privacy requirements.