Long short-term memory recurrent neural networks for signature verification

Abstract

Handwritten signature verification is defined as the classification process that strives to learn the manner in which an individual makes use of the muscular memory of their hands, fingers, and wrist to reproduce a signature. A handwritten signature is captured by a pen input device and sampled at a high frequency which results in time series with several hundred data points. A novel recurrent neural network architecture known as long short-term memory was designed for modeling such a long-time series. This research investigates the suitability of long short-term memory recurrent neural networks for the task of online signature verification. We design and experiment with various network architectures to determine if this model can be trained to discriminate between authentic and fraudulent signatures. We further determine whether the complexity of a signature impacts the performance level of the network when applied to fraudulent signatures. We also investigate the performance level of the network when varying the number of signature features.