Multi-biometric template protection based on bloom filters

Biometric verification systems are currently being deployed in numerous large-scale and everyday applications. It is hence of the utmost importance to protect the privacy of the enrolled subjects. Biometric template protection schemes are designed to protect biometric reference data in an irreversible and unlinkable manner, while maintaining key system properties like the accuracy or the speed. In past years, template protection schemes based on Bloom filters have been introduced and applied to various biometric characteristics. While the irreversibility and unlinkability of Bloom filter-based protection schemes have been shown, their application to any given unprotected template is not straightforward. In this article we present a methodology for the estimation of the main parameters of such schemes, based on a statistical analysis of the unprotected templates. Furthermore, in order to increase verification accuracy and privacy protection, a general approach for a protected weighted feature level fusion is proposed. In order to avoid biased results, the soundness of the estimation methodologies is confirmed for face, iris, fingerprint and fingervein over two totally different sets of publicly available databases. In addition, we show how the weighted feature level fusion preserves the accuracy of the unprotected score level fusion, while it adds privacy protection to the system.