  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%.
