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 sensitive documents by means of the embedded handwritten signatures. The authors have recently proposed the first OSFV implementation, where a machine learning approach based on the dissimilarity representation concept is employed to select a reliable feature representation adapted for the fuzzy vault scheme. In this paper, 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. As the average system entropy is increased from 45-bits to about 51-bits, the AER (average error rate) is decreased by about 21%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIBIM.2014.7015442 | Computational Intelligence in Biometrics and Identity Management |
Keywords | Field | DocType |
handwriting recognition,image representation,learning (artificial intelligence),private key cryptography,aer,brazilian pucpr signature database,gpds signature database,osfv,average error rate,average system entropy,bio-cryptographic,biometrics,dissimilarity representation concept,feature representation,handwritten signature images,key-size adaptation method,machine learning,offline signature-based fuzzy vault,private cryptographic keys,security,indexes,vectors,decoding,cryptography,digital signatures,feature extraction | Data mining,Authentication,Cryptography,Computer science,Word error rate,Digital signature,Artificial intelligence,Password,Biometrics,Machine learning,Key size,Key (cryptography) | Conference |
Citations | PageRank | References |
2 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George S. Eskander | 1 | 35 | 1.66 |
| Robert Sabourin | 2 | 908 | 61.89 |
| E. Granger | 3 | 91 | 16.62 |