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Bio-Cryptographic systems enforce authenticity of cryptogra-phic applications like data encryption and digital signatures. Instead of simple user passwords, biometrics, such as, fingerprint and handwritten signatures, are employed to access the cryptographic secret keys. The Fuzzy Vault scheme (FV) is massively employed to produce bio-cryptogra-phic systems, as it absorbs variability in biometric signals. However, the FV design problem is not well formulated in the literature, and different approaches are applied for the different biometric traits. In this paper, a generic FV design approach, that could be applied to different biometrics, is introduced. The FV decoding functionality is formulated as a simple classifier that operates in a dissimilarity representation space. A boosting feature selection (BFS) method is employed for optimizing this classifier. Application of the proposed approach to offline signature biometrics confirms its viability. Experimental results on the Brazilian signature database (that includes various forgeries) have shown FV recognition accuracy of 90% and system entropy of about 69-bits.

A Dissimilarity-Based Approach for Biometric Fuzzy Vaults–Application to Handwritten Signature Images