Biometric Signature Verification Using Recurrent Neural Networks
This work addresses biometric security for identity verification, but it is incremental as it applies an existing RNN method to a specific domain.
The paper tackled the problem of online signature verification by proposing an LSTM-based Siamese RNN system, which achieved a 17.76% to 28.00% relative improvement in verification performance for skilled forgeries on the BiosecurID benchmark.
Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-the-art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeries.