Combining Classifiers in the ROC-space for Off-line Signature Verification

In this work we present a strategy for off-line signature verification. It takes into account a writer-independent model which reduces the pattern recognition problem to a 2-class problem, hence, makes it possible to build robust signature verification systems even when few signatures per writer are available. Receiver Operating Characteristic (ROC) curves are used to improve the performance of the proposed system. The contribution of this paper is two-fold. First of all, we analyze the impacts of choosing different fusion strategies to combine the partial decisions yielded by the SVM classifiers. Then ROC produced by different classifiers are combined using maximum likelihood analysis, producing an ROC combined classifier. Through comprehensive experiments on a database composed of 100 writers, we demonstrate that the ROC combined classifier based on the writer-independent approach can reduce considerably false rejection rate while keeping false acceptance rates at acceptable levels.