Hybrid Off-Line Handwritten Signature Verification Based on Artificial Immune Systems and Support Vector Machines

Abstract

This paper proposes a new handwritten signature verification method based on a combination of an artificial immune algorithm with SVM. In a first step, the Artificial Immune Recognition System (AIRS) is trained to develop a set of representative data (memory cells) of both genuine and forged signature classes. Usually, to classify a questioned signature, dissimilarities are calculated with respect to all memory cells and handled according to the k Nearest Neighbor rule. Presently, we propose the training of these dissimilarities by a Support Vector Machine (SVM) classifier to get a more discriminating decision. Histogram of oriented gradients is used for feature generation. Experiments conducted on two standard datasets reveal that the proposed system provides a significant accuracy improvement compared to the conventional AIRS.