Share Email Print

Proceedings Paper

Data fusion approach to verifying handwritten signatures on bank checks
Author(s): D. J. Scott; Otman A. Basir; Khaled S. Hassanein; John S. Zelek
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Static signature verification is a well researched problem that has not been completely solved to this date. To improve on current verification performance this research uses a pooling method which fuses together decisions of selected verification algorithms. To enhance this performance further, the decision from this method is fused with the decision of a neural network classifier. This neural network classifier offers a new approach to signature verification, since it is based on recognition techniques. The advantage of this classifier is that it incorporates different information into its decision and therefore allows the fused decision to be based on more diverse information. In contrast to other methods, this classifier requires only genuine signature samples to be trained. Experimental results show that the fusion of verification algorithms can produce better performance than any of the used methods individually.

Paper Details

Date Published: 29 January 1999
PDF: 12 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339810
Show Author Affiliations
D. J. Scott, Univ. of Guelph (Canada)
Otman A. Basir, Univ. of Guelph (Canada)
Khaled S. Hassanein, NCR (Canada)
John S. Zelek, Univ. of Guelph (Canada)

Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?