Reliable on-line human signature verification system for point-of-sales applications

Online dynamic signature verification systems were designed and tested. Data acquisition consisted of compiling a database of more than ten thousand signatures in (x(t), y(t))-form using a graphics tablet. For feature extraction we started with a 42-parameter feature set and advanced to a set of 49 normalized features. The normalized features tolerate inconsistencies in genuine signatures while retaining the power to discriminate against forgeries. For decision making we studied several classifiers types. Specifically, a modified versions of our so-called majority classifier yielded 2.5% equal error rate and, more importantly, an asymptotic performance of 7% false acceptance rate at zero false rejection rate using only 15 parameter features.