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Inspired by the great success of recurrent neural networks (RNNs) insequential modeling, we introduce a novel RNN system to improve the performanceof online signature verification. The training objective is to directlyminimize intra-class variations and to push the distances between skilledforgeries and genuine samples above a given threshold. By back-propagating thetraining signals, our RNN network produced discriminative features with desiredmetrics. Additionally, we propose a novel descriptor, called thelength-normalized path signature (LNPS), and apply it to online signatureverification. LNPS has interesting properties, such as scale invariance androtation invariance after linear combination, and shows promising results inonline signature verification. Experiments on the publicly available SVC-2004dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).

Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature