A robust offline handwritten signature verification system using writer independent approach

In this work, a writer independent offline handwritten signature verification model, also known as global model, for signature verification is proposed. Three classifiers, two back propagation Artificial Neural Networks and a Support Vector Machine with polynomial kernel are probed to develop the global model. Two databases of signatures from different writers are used to evaluate the performance of these classifiers in terms of false acceptance rate and false rejection rate. To develop the system geometric features and local binary pattern features are investigated. It is found in the study that Support Vector Machine outperforms the Artificial Neural Networks in developing handwritten signature verification system using geometric features and local binary pattern features.