Recurrent Neural Networks for Signature Verification

This paper investigates the application of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) to the task of signature verification. Traditional RNNs are capable of modeling dynamical systems with hidden states; they have been successfully applied to domains ranging from financial forecasting to control and speech recognition. We have applied on-line signature time series data to traditional LSTM, LSTM with forget gates and LSTM with peephole connections algorithms which was developed by [4]. It can be clearly seen in this pattern classification problem that traditional LSTM RNNs outperform LSTMs with forget gates and peephole connections. The latter also outperform traditional RNNs which can’t seem to even learn this task due to the long-term dependency problem.