Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature
This work addresses security authentication problems for users by improving verification accuracy, though it is incremental as it builds on existing RNN methods.
The paper tackled online signature verification by introducing a recurrent neural network system and a novel length-normalized path signature descriptor, achieving a state-of-the-art equal error rate of 2.37% on the SVC-2004 dataset.
Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification. LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification. Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).