The fuzzy vault scheme has been established as cryptographic primitive suitable for privacy-preserving biometric authentication. To improve accuracy and privacy protection, biometric information of multiple characteristics can be fused at feature level prior to locking it in a fuzzy vault. We construct a multi-biometric fuzzy vault based on face and multiple fingerprints. On a multi-biometric database constructed from the FRGCv2 face and the MCYT-100 fingerprint databases, a perfect recognition accuracy is achieved at a false accept security above 30 bits. Further, we provide a formalisation of feature-level fusion in multi-biometric fuzzy vaults, on the basis of which relevant security issues are elaborated. Said security issues, for which we define countermeasures, are commonly ignored and may impair the overall system's security.
翻译:为了改进准确性和隐私保护,在将多种特征的生物鉴别信息锁在一个模糊的保险库之前,可以先在特性层上将多种特征的生物鉴别信息连接起来,然后将其锁在一个模糊的保险库中。我们根据面孔和多指纹建造了一个多生物测量的模糊保险库。在从FRGCv2和MCYT-100指纹数据库中建立的一个多生物测量数据库中,在超过30位的虚假接受安全时,可以实现完全的识别准确性。此外,我们还将多生物测量引信保险库的特性级融合正规化,在此基础上,将相关的安全问题阐述出来。我们界定反措施的所谓安全问题通常被忽视,并可能损害整个系统的安全。