Name
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%. |
Year | Venue | DocType |
|---|---|---|
2014 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1408.3985 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George S. Eskander | 1 | 36 | 4.51 |
| Robert Sabourin | 2 | 908 | 61.89 |
| Eric Granger | 3 | 298 | 16.04 |