Off-line signature verification based on deformable grid partition and Hidden Markov Models

A Hidden Markov Model (HMM) approach to off-line signature verification is presented. First, each of the signature images is represented as a landmark point set, which includes turning points, isolated points, trifurcate points, intersection points and termination points on signature skeleton. Then we propose a novel deformable grid partition technique. Based on landmark point matching, we build the matching relations between planar regions to get the deformable grids, and then extract grid features from them. By using HMM in signature modeling, the deformable grid partition method shows remarkable improvements over traditional grid partition methods in discriminative ability.