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

Loading...
Thumbnail Image
File version
Author(s)
Armand, Stephane
Blumenstein, Michael
Muthukkumarasamy, Vallipuram
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2007
Size

2089428 bytes

File type(s)

application/pdf

Location
License
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

Journal Title

IEEE Computational Intelligence Magazine

Conference Title
Book Title
Edition
Volume

2

Issue

2

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Item Access Status
Note
Access the data
Related item(s)
Subject

Electrical and Electronic Engineering

Persistent link to this record
Citation
Collections