A new approach to the problem of handwritten signature verification is presented. This method exploits the regularity of length and curvature of a signature. Overall signature content at various angles is evaluated to form a slope histogram. Histograms are then passed to a classifier constructed from 10 valid signatures. Performance of the classifier on a data pool of 1000 valid and casually forged signatures is evaluated. In particular the equal error rate of this approach is shown to average 7 across 9 different subjects. Increases in the classifier error rates are noted when the forger is allowed some a priori knowledge of the target signature.

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1384, High-Speed Inspection Architectures, Barcoding, and Character Recognition, (1 February 1991); doi: 10.1117/12.25331

Published in SPIE Proceedings Vol. 1384:
High-Speed Inspection Architectures, Barcoding, and Character Recognition
Michael J. W. Chen, Editor(s)