It is both crucial and challenging to protect biometric data used for biometric identification and authentication
systems, while keeping the systems user friendly. We study the design and analysis of biometric data protection
schemes based on fuzzy extractors. There are limitations in previous fuzzy extractors, which make them difficult
to handle continuous feature spaces, entropy estimation, and feature selection. We proposed a scheme based on
PCA features and a recently proposed fuzzy extractor for continuous domains. We conduct experiments using
the ORL face database, and analyze carefully the entropies and the resulting security of the system. We explore
and compare different ways to select and combine features, and show that randomization plays an important
role in both security, performance and cancelability. Furthermore, proposed feature selection does yield better
estimation of the final key strength.
In addition to the inherent qualities that biometrics posses, powerful signal processing tools enabled widespread
deployment of the biometric-based identification/verification systems. However, due to the nature of biometric
data, well-established cryptographic tools (such as hashing, encryption, etc.) are not sufficient for solving one of
the most important problems related to biometric systems, namely, template security. In this paper, we examine
and show how to apply a recently proposed secure sketch scheme in order to protect the biometric templates.
We consider face biometrics and study how the performance of the authentication scheme would be affected after
the application of the secure sketch. We further study the trade-off between the performance of the scheme and
the bound of the entropy loss from the secure sketch.
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