Enhancing Offline Signature Verification via Transfer Learning and Deep Neural Networks

Abstract

This paper presents a brief overview of signature identification and verification systems based on transfer learning. Different databases, namely CEDAR, ICDAR-2011, and BHSig260, are utilized for this study. In the field of biometrics and forensics, automated signature verification plays a crucial role in validating a person’s authenticity. The signature can be offline (handwritten) or online (digital). This study mainly focuses on offline signatures forged by the skilled forgers because offline systems lack dynamic information such as pressure and velocity available in online systems. The offline signatures are analyzed on pretrained models, and their efficiency is analyzed on two critical metrics in the field of biometrics and security systems, namely false acceptance rate (FAR) and false rejection rate (FRR). InceptionV3 model gives highest accuracy of 99.10% and lowest FRR and FAR of 1.03% and 0.74%.