Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results

TL;DR

This paper reviews and presents new results on an offline signature-based fuzzy vault system that uses machine learning for feature selection, introduces key size adaptation, and demonstrates improved security and accuracy on signature datasets.

Contribution

It reviews the first OSFV implementation and introduces a novel key size adaptation method to enhance security and accuracy.

Findings

Key size adaptation increases entropy from 45 to 51 bits.

Average error rate decreases by approximately 21%.

System achieves a good balance between security and accuracy.

Abstract

An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic implementation that uses handwritten signature images as biometrics instead of traditional passwords to secure private cryptographic keys. Having a reliable OSFV implementation is the first step towards automating financial and legal authentication processes, as it provides greater security of confidential documents by means of the embedded handwritten signatures. The authors have recently proposed the first OSFV implementation which is reviewed in this paper. In this system, a machine learning approach based on the dissimilarity representation concept is employed to select a reliable feature representation adapted for the fuzzy vault scheme. Some variants of this system are proposed for enhanced accuracy and security. In particular, a new method that adapts user key size is presented. Performance of proposed methods are compared using the Brazilian PUCPR and GPDS signature databases and results indicate that the key-size adaptation method achieves a good compromise between security and accuracy. While average system entropy is increased from 45-bits to about 51-bits, the AER (average error rate) is decreased by about 21%.