Design and analysis of fuzzy extractors for faces

Abstract

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.