Offline signature verification using shape correspondence

Biometrics has always been an integral part of human identification and verification, with offline signature verification being a most crucial component of it. It is a challenging task as the signatures are time variant. To address the above difficulty, this paper presents a novel approach to identify the correspondence between pixels of different signatures using an adaptive weighted combination of shape context distance and Euclidean distance. These correspondences are then used for the transformation of query signature plane to reference signature plane using thin plate spline transformation. The distances between signatures are computed using plane transformation, a shape descriptor, and the farness between matched pixels. The computed distances are then fed to the support vector machine (SVM) classifier to determine the merit of genuineness. With the proposed methodology, better accuracy is obtained. The results exhibit an accuracy of 89.58% using proposed method on GPDS synthetic signature database.