Off-line Signature Verification Using an Enhanced Modified Direction Feature with Single and Multi-classifier Approaches

Authors:
S. Armand;M. Blumenstein;V. Muthukkumarasamy
Affiliations:
-;-;-
Venue:
IEEE Computational Intelligence Magazine
Year:
2007

Citing 0
Cited 5

Offline Signature Verification Using Local Interest Points and Descriptors

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Signature verification (SV) toolbox: Application of PSO-NN

Engineering Applications of Artificial Intelligence
Off-line Signature Verification (SV) using the Chi-square statistics

International Journal of Biometrics
Radial basis function neural network based on PSO with mutation operation to solve function approximation problem

ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Deep learning networks for off-line handwritten signature recognition

CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The principal objective of this paper was to investigate the efficiency of the enhanced version of the MDF feature extractor for signature verification. Investigations adding new feature values to MDF were performed, assessing the impact on the verification rate of the signatures, using six-fold cross validation. Two different neural classifiers were used and two methodologies for verification were applied. The experiments conducted, whereby MDF was merged with the new features, provided very encouraging results