Use of synthetic discriminant functions for handwritten-signature verification

Handwritten-signature verification is treated as a two-class synthetic discriminant function (SDF) problem. Images of valid and casually forged signatures are collected and binarized, using an electronic digitizing camera. Performance of this approach with a small number of valid signatures in the training set is examined, and substantial improvement is demonstrated when forgeries are included in the set. In particular, the equal-error rate for the SDF classifier with forgeries included is shown to average approximately 4% across nine different subjects. The effects of image preprocessing on false acceptance and true rejection rates are examined. The use of alternatives to forged signatures in the training matrix is explored. Finally, SDF performance is shown to deteriorate when the tested forgeries are produced with some a priori knowledge of the target signature.