Rantzsch, Hannes; Yang, Haojin; Meinel, Christoph
Springer International Publishing
The handwritten signature is widely employed and accepted as a proof of a person's identity. In our everyday life, it is often verified manually, yet only casually. As a result, the need for automatic signature verification arises. In this paper, we propose a new approach to the writer independent verification of offline signatures. Our approach, named Signature Embedding, is based on deep metric learning. Comparing triplets of two genuine and one forged signature, our system learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity. Our system ranks best in nearly all evaluation metrics from the ICDAR SigWiComp 2013 challenge. The evaluation shows a high generality of our system: being trained exclusively on Latin script signatures, it outperforms the other systems even for signatures in Japanese script.
Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Porikli, Fatih; Skaff, Sandra; Entezari, Alireza; Min, Jianyuan; Iwai, Daisuke; Sadagic, Amela; Scheidegger, Carlos; Isenberg, Tobias