Abstract
This paper focuses on offline writer-dependent signature verification using a Siamese convolutional neural network. A Siamese network is comprised of equal-weighted twin networks that can be trained to learn a feature space in which similar observations are juxtaposed. To reduce the impact of the high intra-variability of the signature and ensure that the Siamese network is able to learn more effectively, we propose a method of selecting a Reference (REF). Using the proposed reference selection, the accuracy can be increased by 6.4%. By utilizing the GPDS-160 signature data-set, the designed system is able to achieve an accuracy of 94.5%, which is a better result than that achieved by current state-of-the-art writer-dependent techniques.