An extended-shadow-code based approach for off-line signature verification

Evaluates the extended shadow code used as a global feature vector for the signature verification problem. The latter as a shape factor is very easy to implement. Numerical experiments have been made with a signature database of 800 images (20 writers /spl times/ 40 signatures per writer) in the context of random forgeries. In the first experiment, a kNN classifier with voting shows a mean total error rate of 0.01% with k=1. In the second experiment, a minimum distance classifier has been used. The thresholds were evaluated for each of the 20 writers, and the number of reference signatures was varied in the range 1 /spl les/ N/sub ref/ /spl les/ 10. The mean total error rate was below 1.00% with N/sub ref/ = 4 genuine signatures.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>