Article ID Journal Published Year Pages File Type
4969083 Information Fusion 2018 18 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
Authors
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