Deep Transfer Learning based Handwritten Signature Verification

In past few decades, handwritten verification system has got a lot of attention, but that's still a continuing process. Signature verification techniques are being used to determine whether such a signature is real or forged i.e., produced by an impostor. Many enhancements have certainly been suggested in the literature a most notable of which being use of Deep Learning algorithms to learn image attributes from signature images. As an explorative study, we trained models based on the principle of transfer learning using three state-of-the-art CNNs, namely VGG-16, VGG-19 and Alexnet as feature extractors and classifiers. The CEDAR Dataset, the ICDAR 2011 Dataset and the combination of both the CEDAR and the ICDAR 2011 Datasets are the three datasets considered for the proposed methodology. The models showed performance, with the highest precision reaching up to 100%. Among the three models, the Alexnet model exhibited the highest accuracy and lowest training cost.