LAUSR

Existing methods for online signature verification are generally writer independent, as a common set of features is used for all writers during verification. In this paper, we propose a new method of online handwritten signature verification. Our approach is based on the writer dependent feature as well as writer dependent partition. The two decisions namely optimal feature suitable for a writer and a partition to be used for authenticating the writer, they are taken based on the error rate at the training phase. It is difficult for the forger to imitate the shape and dynamic characteristics of the signer at the same time. According to this feature, we propose to decompose signature trajectories depending upon pressure, velocity direction angle, and velocity information and perform verification on the most stable partition. Experimental results demonstrate superiority of our approach in online signature verification in comparison with other schemes.