Off-line signature recognition based on dynamic methods

In this paper we present the work developed on off-line signature verification as a continuation of a previous work 1 using Left-to-Right Hidden Markov Models (LR-HMM) in order to extend those models to the field of static or off-line signature processing using results provided by image connectivity analysis. The chain encoding of perimeter points for each blob obtained by this analysis is an ordered set of points in the space, clockwise around the perimeter of the blob. Two models are generated depending on the way the blobs obtained from the connectivity analysis are ordered. In the first one, blobs are ordered according to their perimeter length. In the second proposal, blobs are ordered in their natural reading order, i.e. from the top to the bottom and left to right. Finally, two LR-HMM models are trained using the (x,y) coordinates of the chain codes obtained by the two mentioned techniques and a set of geometrical local features obtained from them such as polar coordinates referred to the center of ink, local radii, segment lengths and local tangent angle. Verification results of the two techniques are compared over a biometrical database containing skilled forgeries.