Applying Dissimilarity Representation to Off-Line Signature Verification

Abstract

In this paper, a two-stage off-line signature verification system based on dissimilarity representation is proposed. In the first stage, a set of discrete left-to-right HMMs trained with different number of states and codebook sizes is used to measure similarity values that populate new feature vectors. Then, these vectors are input to the second stage, which provides the final classification. Experiments were performed using two different classification techniques - AdaBoost, and Random Subspaces with SVMs - and a real-world signature verification database. Results indicate that the performance is significantly better with the proposed system over other reference signature verification systems from literature.