Reducing False Acceptance Rate in Offline Writer Independent Signature Verification System through Ensemble of Classifiers

Handwritten signature verification is a very challenging and critical task. This work aims at proposing an efficient offline handwritten signature verification model using writer independent approach. The prime focus of this work is on reducing the false acceptance rate of genuine signatures of writers while letting false rejection rate at a satisfactory level through ensemble of classifiers. The k-fold cross validation technique is used to develop ensemble of classifiers. The performance of the ensemble of classifiers, the support vector machine with polynomial kernel, is analyzed using the signature database of writers. The efficacy of geometric and uniform rotation invariant local binary pattern features is investigated to build a reliable writer independent offline handwritten system. The experiments exhibit 0.00 %, 0.00 % and 1.00 % false acceptance rate for random, simple and skilled forgeries, respectively while allowing false rejection rate 5.00 %.