Recognition of human signatures

We used a digitizing tablet to collect handwritten signatures, with five quantities recorded, namely horizontal and vertical pen tip position, pen tip pressure, and pen azimuth and altitude angles. We divided the signature features into visible ones, namely those related to an "image on the paper" and hidden ones, i.e. those using time-related observations. Cluster analysis was applied to segment the feature space into sub-regions of "similar" signatures. The classification function was approximated with the use of neural networks, namely a two-layer sigmoidal perceptron and the RCE network which is a variety of radial-basis network. Both signature classification and signature verification problems are considered.