Online Signature Verification Using Bidirectional Recurrent Neural Network
Chirag Nathwani · 2020
Signature verification is one of the most widely used ways to authenticate any person in today's world. Due to this reason, signature forgery is being widely attempted by attackers. Signature verification is classified into two categories online signature verification and other is offline signature verification. This paper focuses on identifying forgery done during the online signature verification. In this paper, we used the signature verification competition 2004 (SVC2004) dataset for our experiments. In the proposed method we used Discrete Fourier transform which is used to extract distinguishable features between forged and genuine signatures. In the next step, we used Recurrent Neural Networks methods Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) for classification. We used bidirectional LSTM and bidirectional GRU as here we do have past as well as future results. Final results using bidirectional methods outperformed our overall results.