Nonparametric estimators for online signature authentication

We present extensions to our previous work in modelling dynamical processes. The approach uses an information theoretic criterion for searching over subspaces of the past observations, combined with a nonparametric density characterizing its relation to one-step-ahead prediction and uncertainty. We use this methodology to model handwriting stroke data, specifically signatures, as a dynamical system and show that it is possible to learn a model capturing their dynamics for use either in synthesizing realistic signatures and in discriminating between signatures and forgeries even though no forgeries have been used in constructing the model. This novel approach yields promising results even for small training sets.